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Text Classification Using Naive Bayes
 
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This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 81360 Francisco Iacobelli
Neural Models for Information Retrieval
 
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In the last few years, neural representation learning approaches have achieved very good performance on many natural language processing (NLP) tasks, such as language modeling and machine translation. This suggests that neural models may also yield significant performance improvements on information retrieval (IR) tasks, such as relevance ranking, addressing the query-document vocabulary mismatch problem by using semantic rather than lexical matching. IR tasks, however, are fundamentally different from NLP tasks leading to new challenges and opportunities for existing neural representation learning approaches for text. In this talk, I will present my recent work on neural IR models. We begin with a discussion on learning good representations of text for retrieval. I will present visual intuitions about how different embeddings spaces capture different relationships between items and their usefulness to different types of IR tasks. The second part of this talk is focused on the applications of deep neural architectures to the document ranking task. See more at https://www.microsoft.com/en-us/research/video/neural-models-information-retrieval-video/
Views: 2435 Microsoft Research
What is DOCUMENT CLUSTERING? What does DOCUMENT CLUSTERING mean? DOCUMENT CLUSTERING meaning
 
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What is DOCUMENT CLUSTERING? What does DOCUMENT CLUSTERING mean? DOCUMENT CLUSTERING meaning - DOCUMENT CLUSTERING definition - DOCUMENT CLUSTERING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering. Document clustering involves the use of descriptors and descriptor extraction. Descriptors are sets of words that describe the contents within the cluster. Document clustering is generally considered to be a centralized process. Examples of document clustering include web document clustering for search users. The application of document clustering can be categorized to two types, online and offline. Online applications are usually constrained by efficiency problems when compared to offline applications. In general, there are two common algorithms. The first one is the hierarchical based algorithm, which includes single link, complete linkage, group average and Ward's method. By aggregating or dividing, documents can be clustered into hierarchical structure, which is suitable for browsing. However, such an algorithm usually suffers from efficiency problems. The other algorithm is developed using the K-means algorithm and its variants. Generally hierarchical algorithms produce more in-depth information for detailed analyses, while algorithms based around variants of the K-means algorithm are more efficient and provide sufficient information for most purposes.:Ch.14 These algorithms can further be classified as hard or soft clustering algorithms. Hard clustering computes a hard assignment – each document is a member of exactly one cluster. The assignment of soft clustering algorithms is soft – a document’s assignment is a distribution over all clusters. In a soft assignment, a document has fractional membership in several clusters.:499 Dimensionality reduction methods can be considered a subtype of soft clustering; for documents, these include latent semantic indexing (truncated singular value decomposition on term histograms) and topic models. Other algorithms involve graph based clustering, ontology supported clustering and order sensitive clustering. Given a clustering, it can be beneficial to automatically derive human-readable labels for the clusters. Various methods exist for this purpose.
Views: 1133 The Audiopedia
Lecture 59 — Hierarchical Clustering | Stanford University
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Machine Learning: Ranking
 
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Ranking algorithms
Views: 7759 Jordan Boyd-Graber
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 183415 APMonitor.com
Recurrent Binary Embedding for GPU-Enabled Exhaustive Retrieval from Billion-Scale Semantic Vectors
 
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Authors: Ying Shan (Microsoft); Jian Jiao (Microsoft); Jie Zhu (Microsoft); Jc Mao (Microsoft) Abstract: Rapid advances in GPU hardware and multiple areas of Deep Learning open up a new opportunity for billion-scale information retrieval with exhaustive search. Building on top of the powerful concept of semantic learning, this paper proposes a Recurrent Binary Embedding (RBE) model that learns compact representations for real-time retrieval. The model has the unique ability to refine a base binary vector by progressively adding binary residual vectors to meet the desired accuracy. The refined vector enables efficient implementation of exhaustive similarity computation with bit-wise operations, followed by a near- lossless k-NN selection algorithm, also proposed in this paper. The proposed algorithms are integrated into an end-to-end multi-GPU system that retrieves thousands of top items from over a billion candidates in real-time. The RBE model and the retrieval system were evaluated with data from a major paid search engine. When measured against the state-of-the-art model for binary representation and the full precision model for semantic embedding, RBE significantly outperformed the former, and filled in over 80% of the AUC gap in-between. Experiments comparing with our production retrieval system also demonstrated superior performance. While the primary focus of this paper is to build RBE based on a particular class of semantic models, generalizing to other types is straightforward, as exemplified by two different models at the end of the paper. More on http://www.kdd.org/kdd2018/
Views: 109 KDD2018 video
Understanding Underwater Video Content - National Infrastructure Access Programme (SmartBay)
 
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Cabled observatories, such as the one from the SmartBay in Galway Bay, enable continuous sensing in underwater environments providing enormous amounts of raw data; images in this case. Such quantities of data make the extraction of information a very time-consuming task. The long-term goal of this research is to enable higher-level knowledge exploitation of underwater imagery by semantic labeling and indexing of visual data to improve data mining and retrieval. Object segmentation algorithms were applied to the data captured from the HDTV camera attached to the SmartBay Cabled Observatory. The results can be used to automatically identify potentially interesting video segments thereby reducing the amount of video content that needs to be stored or subsequently analysed. The results have also shown the potential of coupling the state-of-the-art deep learning algorithms with the world leading marine test facility to explore the hidden world under the sea.
Machine Learning - Text Classification with Python, nltk, Scikit & Pandas
 
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In this video I will show you how to do text classification with machine learning using python, nltk, scikit and pandas. The concepts shown in this video will enable you to build your own models for your own use cases. So let's go! _About the channel_____________________ TL;DR Awesome Data science with very little math! -- Hello I'm Jo the “Coding Maniac”! On my channel I will show you how to make awesome things with Data Science. Further I will present you some short Videos covering the basic fundamentals about Machine Learning and Data Science like Feature Tuning, Over/Undersampling, Overfitting, ... with Python. All videos will be simple to follow and I'll try to reduce the complicated mathematical stuff to a minimum because I believe that you don't need to know how a CPU works to be able to operate a PC... GitHub: https://github.com/coding-maniac _Equipment _____________________ Camera: http://amzn.to/2hkVs5X Camera lens: http://amzn.to/2fCEU9z Audio-Recorder: http://amzn.to/2jNu2KJ Microphone: http://amzn.to/2hloKBG Light: http://amzn.to/2w8J92N _More videos _____________________ More videos in german: https://youtu.be/rtyJyzqeByU, https://youtu.be/1A3JVSQZ4N0 Subscribe "Coding Maniac": https://www.youtube.com/channel/UCG0TtnkdbMvN5OYQcgNFY1w More videos on "Coding Maniac": https://www.youtube.com/channel/UCG0TtnkdbMvN5OYQcgNFY1w _Social Media_____________________ ►Facebook: https://www.facebook.com/codingmaniac/ _____________________
Views: 13821 Coding-Maniac
Text By the Bay 2015: Jeff Sukharev, Machine Translation Approach for Name Matching in Record Link
 
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Record linkage, or entity resolution, is an important area of data mining. Name matching is a key component of systems for record linkage. Alternative spellings of the same name are a common occurrence in many applications. We use the largest collection of genealogy person records in the world together with user search query logs to build name- matching models. The procedure for building a crowd-sourced training set is outlined together with the presentation of our method. We cast the problem of learning alternative spellings as a machine translation problem at the character level. We use information retrieval evaluation methodology to show that this method substantially outperforms on our data a number of standard well known phonetic and string similarity methods in terms of precision and recall. Our result can lead to a significant practical impact in entity resolution applications. BS, MS Computer Science UC Santa Cruz, PhD candidate Computer Science UC Davis. Senior Data Scientist at Ancestry.com working on record linkage applications. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI
Views: 215 FunctionalTV
Towards Contextual Text Mining
 
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Text is generally associated with all kinds of contextual information. Contextual information can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when he/she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of patterns of topics over different contexts. For instance, analysis of search logs in the context of users can reveal how we can improve the quality of a commercial search engine by optimizing the search results according to particular users, while analysis of text in the context of a social network can facilitate discovery of more meaningful topical communities. Since contextual information affects significantly the choices of topics and words made by authors, in general, it is very important to incorporate it in analyzing and mining text data. In this talk, I will present a new paradigm of text mining, called contextual text mining, where context is treated as a first-class
Views: 72 Microsoft Research
Excel spreadsheet with macros for (super quick) categorizing of data.
 
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The Microsoft Excel spreadsheet available for download at https://www.legaltree.ca/node/2225 contains macros that allow the user to rapidly categorize data. This is done by way of a form that allows one-click entering of category labels into a column in Excel. The different categories of data can then be tallied using the Sumif formula, or used in various other ways. One obvious application for this spreadsheet is to categorize and tally household expenses, but it could be used for any situation in which a user wishes to categorize / label / tag data into 75 or fewer categories.
Views: 653 Michael Dew
Labeling and modeling large databases of videos
 
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Ground truth data is useful for training, experimentation, and benchmarking; however, video annotation encompasses additional challenges compared to its static image counterpart. For instance, if the objective is to annotate an object in a video, the user has to delineate its spatio-temporal extent at each frame in the video. Nonetheless, if the objective is event annotation, standard object annotation methods might not be sufficient as an event can consist of one or multiple objects interacting with each other in potentially complex ways. The first component of this thesis plans to address this problem by engineering and deploying a video annotation tool. A second and major component consists of developing methods for learning and integrating information from large databases of potentially heterogeneous video. Currently, video surveillance technologies for anomaly identification assume the availability of hours or days of video data originating from the location where the system will be deployed. However, in the parallel fields of object and scene recognition, research has reached breakthrough advancements transitioning from instance to class recognition and classification. One objective of this thesis is to extend these well studied technologies and adapt them to scene instances that have not been seen previously. Scene-matching techniques are used at the video frame level to perform video retrieval given a query video or image and a large database of videos captured at different scenes. The data from the nearest neighbors is then be integrated to compile a summary of what is likely to happen in scenes similar to the query, generating motion predictions for the image.
Views: 1122 Jenny Yuen
DATA MINING   3 Text Mining and Analytics   3 9 Latent Dirichlet Allocation LDA Part 1
 
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https://www.coursera.org/learn/text-mining
Views: 120 Ryo Eng
Sanghamitra Deb | Creating Knowledgebases from unstructured text
 
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PyData SF 2016 NLP and Machine Learning without training data. A major part of Big Data collected in most industries is in the form of unstructured text. Some examples are log files in IT sector, analysts reports in the finance sector, patents, laboratory notes and papers, etc. Some of the challenges of gaining insights from unstructred text is converting it into structured information and generating training sets for machine learning. Typically training sets for supervised learning are generated through the process of human annotation. In case of text this involves reading several thousands to million lines of texts by subject matter experts. This is very expensive and may not always be available, hence it is important to solve the problem of generating training sets before attempting to build machine learning models. Our approach is to combine rule based techniques with small amounts of SME time to by pass time consuming manual creation of training data. Once we have a good set of rules mimicking the training data we will use them to create knowledgebases out of the structured data. This knowledgebase can be further queried to gain insight on the domain. I have applied this technique to several domains, such as data from drug labels and medical journals, log data generated through customer interaction, generation of market research reports, etc. I will talk about the results in some of these domains and the advantage of using this approach.
Views: 1350 PyData
Naive Bayes for Text Classification - Part 1/3
 
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This is PART 1 OF 3 videos that explains an example of how Naive Bayes classifies text documents and its implementation with scikit-learn. The example has been adapted from the relevant portion of the textbook by Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008. LINK TO THE RELEVANT PORTION (TEXT CLASSIFICATION WITH NAIVE BAYES): https://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html This video has not been monetized and does not promote any product.
Views: 241 Abhishek Babuji
iOS Swift Tutorial: Guide to Using JSON Data from the Web
 
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If your app communicates with a web application, information returned from the server is often formatted as JSON. By getting weather data from the darksky API you are going to learn how to retrieve and effectively deal with JSON Data. ➡️ Web: http://www.brianadvent.com ➡️ Tutorial Files https://github.com/brianadvent/JSONBasics ✉️ COMMENTS ✉️ If you have questions about the video or Cocoa programming, please comment below.
Views: 38444 Brian Advent
Semantic Indexing of Unstructured Documents Using Taxonomies and Ontologies
 
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From August 7, 2013 Life Science and Healthcare organizations use RDF/SKOS/OWL based vocabularies, thesauri, taxonomies and ontologies to organize enterprise knowledge. There are many ways to use these technologies but one that is gaining momentum is to semantically index unstructured documents through ontologies and taxonomies. In this talk we will demonstrate two projects where we use a combination of SKOS/OWL based taxonomies and ontologies, entity extraction, fast text search, and Graph Search to create a semantic retrieval engine for unstructured documents. The first project organized all science related artifacts in Malaysia through a taxonomy of scientific concepts. It indexed all papers, people, patents, organizations, research grants, etc, etc, and created a user friendly taxonomy browser to quickly find relevant information, such as, "How much research funding has been spent on a certain subject over the last 3 years and how many patents resulted from this research". The second project discusses a large socio-economic content publisher that has millions of documents in at least eight different languages. Reusing documents for new publications was a painful process given that keyword search and LSI techniques were mostly inadequate to find the document fragments that were needed. Fortunately the organization had begun developing a large SKOS based taxonomy that linked common concepts to various preferential and alternative labels in many languages. We used this taxonomy to index millions of document fragments and we'll show how we can perform relevancy search and retrieval based on taxonomic concepts.
Views: 5764 AllegroGraph
Evaluating Classifiers: Confusion Matrix for Multiple Classes
 
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Confusion Matrix for Multiple Classes www.imperial.ac.uk/people/n.sadawi
Views: 45270 Noureddin Sadawi
Building Search Strategies - Selected text analysis tools: part 1
 
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Julie Glanville, Associate Director of the systematic reviews and information services workstream at YHEC, discusses the use of PubMed PubReMiner and GoPubMed in building search strategies
Good Linear Decision Surface - Intro to Machine Learning
 
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This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 32224 Udacity
Manik Varma: Extreme Multi-label Loss Functions for Tagging, Ranking & Recommendation
 
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Talk at the NIPS Workshop on Multi-class and Multi-label Learning in Extremely Large Label Spaces
Views: 1236 Manik Varma
CSCI572 - Information Retrieval and Web Search Engines - Team 31
 
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This video provides a demo for the third assignment in this course at USC. This assignment requires us to provide data visualization capabilities over data indexed in Solr. The team members are: Prerna Dwivedi Hetal Mandavia Leena Tahilramani Prerna Totla
Views: 107 Prerna Totla
Text Classification - Natural Language Processing With Python and NLTK p.11
 
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Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label. To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment. Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
Views: 84896 sentdex
Hierarchical Multilabel Classification and Voting for Genre Classification
 
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Presenter: Maximilian Mayerl Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_41.pdf Slides: https://www.slideshare.net/multimediaeval/mediaeval-2017-acousticbrainz-genre-task-hierarchical-multilabel-classification-and-voting-for-genre-classification Authors: Benjamin Murauer, Maximilian Mayerl, Michael Tschuggnall, Eva Zangerle, Martin Pichl, Günther Specht Abstract: This paper summarizes our contribution (team DBIS) to the AcousticBrainz Genre Task: Content-based music genre recognition from multiple sources as part of MediaEval 2017. We utilize a hierarchical set of multilabel classifiers to predict genres and subgenres and rely on a voting scheme to predict labels across datasets.
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3.
Views: 337789 Thales Sehn Körting
SIGIR 2018:  Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
 
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The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval Ann Arbor Michigan, U.S.A. July 8-12, 2018 Title: Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce Abstract: In recent years, product search engines have emerged as a key factor for online businesses. According to a recent survey, over 55% of online customers begin their online shopping journey by searching on an E-Commerce (EC) website like Amazon as opposed to a generic web search engine like Google. Information retrieval research to date has been focused on optimizing search ranking algorithms for web documents while little attention has been paid to product search. There are several intrinsic differences between web search and product search that make the direct application of traditional search ranking algorithms to EC search platforms difficult. First, the success of web and product search is measured differently; one seeks to optimize for relevance while the other must optimize for both relevance and revenue. Second, when using real-world EC transaction data, there is no access to manually annotated labels. In this paper, we address these differences with a novel learning framework for EC product search called LETORIF (LEarning TO Rank with Implicit Feedback). In this framework, we utilize implicit user feedback signals (such as user clicks and purchases) and jointly model the different stages of the shopping journey to optimize for EC sales revenue. We conduct experiments on real-world EC transaction data and introduce a a new evaluation metric to estimate expected revenue after re-ranking. Experimental results show that LETORIF outperforms top competitors in improving purchase rates and total revenue earned. Authors: Liang Wu http://www.public.asu.edu/~liangwu1/ Liang Wu has been a PhD student of Computer Science and Engineering at Arizona State University since August, 2014. He obtained his master's degree from Chinese Academy of Sciences in 2014 and bachelor's from Beijing Univ. of Posts and Telecom., China in 2011. The focus of his research is in the areas of misinformation and content polluter detection, and statistical relational learning. He has published over 20 innovative works in major international conferences in data mining and information retrieval, such as SIGIR, ICDM, SDM, WSDM, ICWSM, CIKM and AAAI. Liang has participated in various competitions and data challenges and won the Honorable Mention Award of KDD Cup 2012 on predicting click-through rate of search sponsored ads, ranking 3rd on leaderboard. He is also an author of 6 patent applications and 2 book chapters, and he is a tutorial speaker at SBP'16 and ICDM'17. He has been a Research Intern at Microsoft Research Asia and a Data Science Intern at Etsy and Airbnb. Diane Hu http://cseweb.ucsd.edu/~dhu/ Liangjie Hong http://www.hongliangjie.com/ Huan Liu http://www.public.asu.edu/~huanliu/
Views: 216 Liang Wu
Support Vector Machine - Georgia Tech - Machine Learning
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-386608826/m-375838864 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 130425 Udacity
Using Categorical Features in Mining Bug Tracking Systems to Assign Bug Reports
 
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Most bug assignment approaches utilize text classification and information retrieval techniques. These approaches use the textual contents of bug reports to build recommendation models. The textual contents of bug reports are usually of high dimension and noisy source of information. These approaches suffer from low accuracy and high computational needs. In this paper, we investigate whether using categorical fields of bug reports, such as component to which the bug belongs, are appropriate to represent bug reports instead of textual description. We build a classification model by utilizing the categorical features, as a representation, for the bug report. The experimental evaluation is conducted using three projects namely NetBeans, Freedesktop, and Firefox. We compared this approach with two machine learning based bug assignment approaches. The evaluation shows that using the textual contents of bug reports is important. In addition, it shows that the categorical features can improve the classification accuracy. http://www.airccse.org/journal/ijsea/current.html
Views: 5 IJSEA Journal
Bhargav Srinivasa Desikan - Topic Modelling with Gensim
 
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Topic Modelling is an information retrieval technique to identify key topics in a large corpus of text documents. It is a very handy technique to model unstructured textual data, and is used heavily in both industry and in research to both understand trends in textual data and analyse new documents via their topics. Gensim is an open-source python NLP framework which provides an API to do robust, industry-grade Topic Modelling which is memory independent and super fast, while being very simple to use. The best part of gensim and python for Topic Modelling is it’s ease of usage and effectiveness. I would propose a small talk to explain how to effectively do topic modelling in python using Gensim framework- especially - after identifying topics from a large dataset, and then leveraging to perform un-supervised clustering, colouring topic-words in a document, and better understanding textual data for subsequent usage. All of this will be supported with examples from research and industry. [My relationship with Gensim is through the Google Summer of Code 2016 program, where I am implementing Dynamic Topic Models for them]
Views: 1902 PyCon SK
Local Modeling of Attributed Graphs: Algorithms and Applications
 
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Author: Bryan Perozzi, Computer Science Department, Stony Brook University Abstract: It is increasingly common to encounter real-world graphs which have attributes associated with the nodes, in addition to their raw connectivity information. For example, social networks contain both the friendship relations as well as user attributes such as interests and demographics. A protein-protein interaction network may not only have the interaction relations but the expression levels associated with the proteins. Such information can be described by a graph in which nodes represent the objects, edges represent the relations between them, and feature vectors associated with the nodes represent the attributes. This graph data is often referred to as an attributed graph. This thesis focuses on developing scalable algorithms and models for attributed graphs. This data can be viewed as either discrete (set of edges), or continuous (distances between embedded nodes), and I examine the issue from both sides. Specifically, I present an online learning algorithm which utilizes recent advances in deep learning to create rich graph embeddings. The multiple scales of social relationships encoded by this novel approach are useful for multi-label classification and regression tasks in networks. I also present local algorithms for anomalous community scoring in discrete graphs. These algorithms discover subsets of the graph’s attributes which cause communities to form (e.g. shared interests on a social network). The scalability of all the methods in this thesis is ensured by building from a restricted set of graph primitives, such as ego-networks and truncated random walks, which exploit the local information around each vertex. In addition, limiting the scope of graph dependencies we consider enables my approaches to be trivially parallelized using commodity tools for big data processing, like MapReduce or Spark. The applications of this work are broad and far reaching across the fields of data mining and information retrieval, including user profiling/demographic inference, online advertising, and fraud detection. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 67 KDD2017 video
What Is Tree Pruning In Data Mining?
 
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Study of various decision tree pruning methods semantic scholar. 31 dec 2015 i understood what is decision trees and how it works with help of sunil sir's i couldnt understand what is pruning and how to do it in decision trees. I am however slightly uncertain in exactly how cv is used when pruning decision 4 mar 2016 lecture 77 (optional) tree algorithm and full of visualizations illustrations these techniques will behave on real data. Data training_imp_var,method there are several approaches to avoiding overfitting in building decision trees. Decision trees (part ii pruning the tree) ismll. What is tree pruning in data mining? Youtubepruning decision trees ibmwhat and how to do mining mapdata with. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances data mining induction learn simple and easy steps pruning performed order remove anomalies training 8 jul 2017. We may get a decision tree that might perform attribute selection measures, tree, post pruning, pre pruningdata mining is the extraction of hidden predictive information matteo matteucci retrieval & data mining• Usually based on statistical significance test. Pre pruning that stop growing the tree earlier, before it perfectly classifies this thesis presents algorithms for decision trees and lists are based should prove useful in practical data mining applications response to problem of overfitting nearly all modern adopt a strategy some sort. Test data keywords decision tree, tree pruning, miningdecision is one of the classification technique used in support system and i think have understood concepts between cross validation pruning. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia. Pruning (decision trees) wikipedia. Rpart rpart(promotion_name. Decision trees pruning matteo matteuccioverfitting of decision tree and in data mining techniques ijltet. Googleusercontent search. Data mining pruning (a decision tree, rules) [gerardnico]. Data mining cross validations and decision tree pruning (optional) algorithm university of washington. Many algorithms use a technique 26 nov 2008 lack of data points in the lower half diagram makes it difficult to predict correctly class labels that region. Insufficient number of 13 oct 2013 a decision tree is pruned to get (perhaps) that generalize better independent test data. Wikipedia wiki pruning_(decision_trees)&sa u&ved 0ahukewjmxtqoi fvahutr48khypnaloqfggjmae&usg afqjcnhlpev_pbfseaco7iybewg5c15a3w"pruning (decision trees) wikipedia pruning wikipedia en. Pruning is a technique in machine learning that reduces the size of decision trees by removing sections tree provide little power to classify instances. • Stop growing the tree when there is no data& regression• If a decision tree is decision tree pruning methodologies. D
Views: 182 Evelina Hornak Tipz
Proactive Learning and Structural Transfer Learning: Building Blocks of Cognitive Systems
 
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Dr. Jaime Carbonell is an expert in machine learning, scalable data mining (“big data”), text mining, machine translation, and computational proteomics. He invented Proactive Machine Learning, including its underlying decision-theoretic framework, and new Transfer Learning methods. He is also known for the Maximal Marginal Relevance principle in information retrieval. Dr. Carbonell has published some 350 papers and books and supervised 65 Ph.D. dissertations. He has served on multiple governmental advisory committees, including the Human Genome Committee of the National Institutes of Health, and is Director of the Language Technologies Institute. At CMU, Dr. Carbonell has designed degree programs and courses in language technologies, machine learning, data sciences, and electronic commerce. He received his Ph.D. from Yale University. For more, read the white paper, "Computing, cognition, and the future of knowing" https://ibm.biz/BdHErb
Views: 1689 IBM Research
Getting Started with Weka - Machine Learning Recipes #10
 
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Hey everyone! In this video, I’ll walk you through using Weka - The very first machine learning library I’ve ever tried. What’s great is that Weka comes with a GUI that makes it easy to visualize your datasets, and train and evaluate different classifiers. I’ll give you a quick walkthrough of the tool, from installation all the way to running experiments, and show you some of what it can do. This is a helpful library to have while you’re learning ML, and I still find it useful today to experiment with new datasets. Note: In the video, I quickly went through testing. This is an important topic in ML, and how you design and evaluate your experiments is even more important than the classifier you use. Although I publish these videos at turtle speed, I’ve started working on an experimental design one, and that’ll be next! Also, we will soon publish some testing tips and best practices on tensorflow.org (https://goo.gl/nZcS5R). Links from the video: Weka → https://goo.gl/2TYjGZ Ready to use datasets → https://goo.gl/PM8DtH More on evaluating classifiers, particularly in the medical domain → https://goo.gl/TwTYyk Check out the Machine Learning Recipes playlist → https://goo.gl/KewA03 Follow Josh on Twitter → https://twitter.com/random_forests Subscribe to the Google Developers channel → http://goo.gl/mQyv5L
Views: 41449 Google Developers
HCIR 2011: Human Computer Information Retrieval - Presentation I
 
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HCIR 2011 The Fifth Workshop on Human-Computer Interaction and Information Retrieval October 20, 2011 Mountain View, CA Morning Session Presentations I more info at: http://hcir.info/hcir-2011
Views: 1483 GoogleTechTalks
IEEE 2014 MATLAB Mining Weakly Labeled Web Facial Images for Search Based Face Annotation
 
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PG Embedded Systems #197 B, Surandai Road Pavoorchatram,Tenkasi Tirunelveli Tamil Nadu India 627 808 Tel:04633-251200 Mob:+91-98658-62045 General Information and Enquiries: [email protected] [email protected] PROJECTS FROM PG EMBEDDED SYSTEMS 2014 ieee projects, 2014 ieee java projects, 2014 ieee dotnet projects, 2014 ieee android projects, 2014 ieee matlab projects, 2014 ieee embedded projects, 2014 ieee robotics projects, 2014 IEEE EEE PROJECTS, 2014 IEEE POWER ELECTRONICS PROJECTS, ieee 2014 android projects, ieee 2014 java projects, ieee 2014 dotnet projects, 2014 ieee mtech projects, 2014 ieee btech projects, 2014 ieee be projects, ieee 2014 projects for cse, 2014 ieee cse projects, 2014 ieee it projects, 2014 ieee ece projects, 2014 ieee mca projects, 2014 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2014 mtech projects, 2014 mphil projects, 2014 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2014 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2014 ieee omnet++ projects, ieee 2014 oment++ project, innovative ieee projects, latest ieee projects, 2014 latest ieee projects, ieee cloud computing projects, 2014 ieee cloud computing projects, 2014 ieee networking projects, ieee networking projects, 2014 ieee data mining projects, ieee data mining projects, 2014 ieee network security projects, ieee network security projects, 2014 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2014 wireless networking projects ieee, 2014 ieee web service projects, 2014 ieee soa projects, ieee 2014 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2014 IEEE java projects,2014 ieee Project Titles, 2014 IEEE cse Project Titles, 2014 IEEE NS2 Project Titles, 2014 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2014 - 2014 ... Image Processing. IEEE 2014 - 2014 Projects | IEEE Latest Projects 2014 - 2014 | IEEE ECE Projects2014 - 2014, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2014 IEEE C#, C Sharp Project Titles, 2014 IEEE EmbeddedProject Titles, 2014 IEEE NS2 Project Titles, 2014 IEEE Android Project Titles. 2014 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2014, IEEE 2014 PROJECT TITLES, M.TECH. PROJECTS 2014, IEEE 2014 ME PROJECTS.
Views: 673 PG Embedded Systems
R2 DAY2-03 Information extraction with Python - jiawei chen (PyCon APAC 2015)
 
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Speaker: jiawei chen This talk will present a named entity recognition (NER) system for extracting attributes and values, like person, company, place or time, from various of text data. I will introduce how to combine several python tools to build this system. First, use a python written annotation tool BRAT to create a custom annotated corpus. Second, use python to link CRFsuite, training a Conditional Random Fields model to labeling our list of text data, the labeling result will be further analyzed by pandas and scikit-learn. About the speaker A search engineer, usually like to study machine learning and natural language processing. 頭銜 search engineer https://tw.pycon.org/2015apac/zh/program/61
Views: 878 PyCon Taiwan
QDA Miner - Creating a Project from a List of Documents
 
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The easiest method to create a new project and start doing analysis in QDA Miner is by specifying a list of existing documents or images and importing them into a new project. Using this method creates a simple project with two or three variables: A categorical variable containing the original name of the files from which the data originated, a DOCUMENT variable containing imported documents and/or an IMAGE variable containing imported graphics. All text and graphic files are stored in different cases so, if 10 files have been imported, the project will have 10 cases with two or three variables each. To split long documents into several ones or extract numerical, categorical, or textual information from those documents and store them into additional variables, use the Document Conversion Wizard.
You need to store and manage Unstructured data in sql server, what approach would you use
 
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In this video you will learn the answer of SQL Server DBA interview Question "You need to store and manager unstructured data in SQL Server, Which approach you would use it? " Complete list of SQL Server DBA Interview Questions by Tech Brothers http://sqlage.blogspot.com/search/label/SQL%20SERVER%20DBA%20INTERVIEW%20QUESTIONS
Views: 2473 TechBrothersIT
Beating DFARS 7012 with Data Discovery and Classification
 
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Visit us at https://www.spirion.com to view more videos on this topic. Guest speaker Scott Giordano discusses how data discovery and data classification can help bring organizations into compliance with DFARS 7012 NIST SP 800-171 cybersecurity requirements.   October of 2016, the U.S. Department of Defense published the final version of Safeguarding Covered Defense Information and Cyber Incident Reporting (DFARS 252.204-7012).  The rule requires contractors to establish information security controls based on NIST SP 800-171 and to notify the DoD of a cybersecurity breach within 72 hours.  Moreover, these requirements must be flowed down to subcontractors.  Much of the challenge in complying with the rule is in determining where Controlled Unclassified Information (CUI) lies throughout your organization and labeling it in a way that leverages the data protection abilities of data loss prevention (DLP) and other tools you already have in place.  Data Discovery & Classification (DD&C) represents the ability to examine your entire information ecosystem in real time, identify a variety of sensitive data types, and apply the labels that will both assist in meeting the requirements of 800-171 and effectively proving it to prime contractors or the DoD.  With a December 31 deadline looming, getting a compliance program in place has become imperative for many in the aerospace and defense industry.  In this session, industry veterans will offer their perspectives on using DD&C to meet 7012 ahead of the deadline, including:  - Controlled Defense Information (CDI) vs. Controlled Unclassified Information (CUI) and why it matters - DD&C capabilities vs. traditional discovery tools - How DDC fits into NIST SP 800-171 - Rationalizing multiple information security and privacy requirements with one effort Who should attend: Federal employees and contractors in information security and cyber security, also Information Officers including CIOs, Information Security Directors, Staff Attorneys, Privacy and Compliance
Views: 120 Spirion
NLP/Text Analytics: Spark ML & Pipelines, Stanford CoreNLP, Succint, KeystoneML (Part 2)
 
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Advanced Apache Spark Meetup January 12th, 2016 Speakers: Michelle Casbon, Rachit Agarwal and Marek Kolodziej Location: Big Commerce http://www.meetup.com/Advanced-Apache-Spark-Meetup/events/224726467/ Enjoy this "meetup-turned-mini-conference" covering many aspects of Information Retrieval, Search, NLP, and Text-based Advanced Analytics with Spark including the following talks: Part 1 (https://youtu.be/R_SHYey7eas) Training & Serving NLP/Spark ML Models in a Distributed Cloud-based Infrastructure by Michelle Casbon (Idibon) Berkeley AMPLab Project Succinct: Search + Spark by Rachit Agarwal (Berkeley AMPLab) Part 2 Google's Word2Vec and Spark by Marek Kolodziej (Nitro) For more information about the Spark Technology Center: http://www.spark.tc/ Follow us: @apachespark_tc Location: San Francisco, CA Apache®, Apache Spark™, and Spark™ are trademarks of the Apache Software Foundation in the United States and/or other countries.
Views: 821 IBM CODAIT
Lecture 37 — Text Categorization  Methods | UIUC
 
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. Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "FAIR USE" for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. .
Attribute Extraction from Product Titles in eCommerce
 
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Author: Ajinkya More, Wal-Mart Stores, Inc. Abstract: This paper presents a named entity extraction system for detecting attributes in product titles of eCommerce retailers like Walmart. The absence of syntactic structure in such short pieces of text makes extracting attribute values a challenging problem. We find that combining sequence labeling algorithms such as Conditional Random Fields and Structured Perceptron with a curated normalization scheme produces an effective system for the task of extracting product attribute values from titles. To keep the discussion concrete, we will illustrate the mechanics of the system from the point of view of a particular attribute - brand. We also discuss the importance of an attribute extraction system in the context of retail websites with large product catalogs, compare our approach to other potential approaches to this problem and end the paper with a discussion of the performance of our system for extracting attributes. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 746 KDD2016 video
Create Time Slider For Shapefile In Arcmap
 
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Visualizing temporal data can help you step through your data in a temporal sequence and see the patterns or trends that emerge in your data over time. In ArcMap, ArcGlobe, or ArcScene, you can enable time properties on your data and visualize it using a simple time slider that changes the data in the display or in a graph through time.
Views: 1330 GIS 4 YOU
Image Segmentation using the Columbus Image Data Storage and Analysis System
 
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This video shows just how simple accurate image segmentation is with the Columbus™ Image Data Storage and Analysis System For more information about the Columbus System, please visit http://bit.ly/119hpYN
Views: 1206 PerkinElmer, Inc.
Excel 2013 Statistical Analysis #11: Power Query Import Multiple Text Files, Grade Histogram by Year
 
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Download files: http://people.highline.edu/mgirvin/excelisfun.htm Topics in this video: 1. (00:16) Over View of File Import and Histogram Creation 2. (00:56) Look at Zipped Folder from class download then unzip it with Right-click, “Extract All” 3. (01:15) Text Files for communication between databases and data analysis programs like Excel 4. (02:06) Use Power Query to Import Multiple Files 5. (02:10) Get External Data Tab in Power Query, From File Button, From Folder Button 6. (02:33) We only need to keep “Content” Column, so right-click “Content” Field Name and point to “Remove Other Columns” 7. (02:51) To reveal data in imported tables, click the button with the Two Downward Point Arrows. 8. (02:58) Filter out Field Name. 9. (04:10) Name Query 10. (04:17) Close and Load To a cell in our worksheet (this brings table of data from the Power Query editor window into our worksheet) 11. (04:51) Build Frequency Distribution with a PivotTable 12. (05:28) Use Find and Replace feature to create non-ambiguous labels in a Grouped Decimal Number PivotTable. 13. (06:31) Add a Slicer for the Year Variable to the PivotTable 14. (07:26) Create Histogram
Views: 12992 ExcelIsFun
Bugra Akyildiz - A Machine Learning Pipeline with Scikit-Learn
 
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PyData NYC 2014 Scikit-Learn is one of the most popular machine learning library written in Python, it has quite active community and extensive coverage for a number of machine learning algorithms. It has feature extraction, feature and model selection algorithms, and validation methods as well to build a modern machine learning pipeline. This tutorial introduces common recipes to build a modern machine learning pipeline for different input domains and show how one might construct the components using advanced features of Scikit-learn. Specifically, I will introduce feature extraction methods using image and text, and show how one may use feature selection methods to reduce the input dimension space and remove the features which are not useful for classification. For optimization, I will show model selection methods using parameter search. Last in the pipeline, I will show validation methods to be able to choose best parameters. After building the pipeline, I will also show how one might deploy the model into production.
Views: 3414 PyData
Technical Course: Cluster Analysis: K-Means Algorithm for Clustering
 
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K-Means Algorithm for clustering by Gaurav Vohra, founder of Jigsaw Academy. This is a clip from the Clustering module of our course on analytics. Jigsaw Academy is an award winning premier online analytics training institute that aims to meet the growing demand for talent in the field of analytics by providing industry-relevant training to develop business-ready professionals.Jigsaw Academy has been acknowledged by blue chip companies for quality training Follow us on: https://www.facebook.com/jigsawacademy https://twitter.com/jigsawacademy http://jigsawacademy.com/
Views: 197554 Jigsaw Academy