Search results “Data mining and information retrieval label”
Text Classification Using Naive Bayes
This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.
Views: 86160 Francisco Iacobelli
Neural Models for Information Retrieval
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: 3416 Microsoft Research
Machine Learning: Ranking
Ranking algorithms
Views: 8501 Jordan Boyd-Graber
006. Graph-based semi-supervised learning methods: Comparison and tuning - Konstantin Avrachenkov
Semi-supervised learning methods constitute a category of machine learning methods which use labelled points together with the similarity graph for classification of data points into predefined classes. For each class a semi-supervised method provides a classification function. The main idea of the semi-supervised methods is based on the assumption that the classification function should change smoothly over the similarity graph. This idea can be formulated as an optimization problem. Some particularly well known semi-supervised learning methods are the Standard Laplacian (or transductive learning) method and the Normalized Laplacian (or diffusion kernel) method. Different semi-supervised learning methods have different kernels which reflect how the underlying similarity graph influences the values of the classification functions. In the present work, we analyse a general family of semi-supervised methods, explain the differences between the methods and provide recommendations for the choice of the kernel parameters and labelled points. In particular, it appears that it is preferable to choose a method and a kernel based on the properties of the labelled points. We illustrate our general theoretical conclusions with a typical benchmark example, clustered preferential attachment model and two applications. One application is about classification of Wikipedia pages and another application is about classification of content in P2P networks. This talk is based on the joint works with P. Goncalves, A. Mishenin and M. Sokol.
Machine Learning - Text Classification with Python, nltk, Scikit & Pandas
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: 17062 Coding-Maniac
Data mining of your resume
By Tirthankar Dash
Views: 181 Sumana Dash
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: 1261 The Audiopedia
SIGIR 2018:  Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
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: 291 Liang Wu
Lecture 59 — Hierarchical Clustering | Stanford University
. 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. .
Getting Started with Orange 17: Text Clustering
How to transform text into numerical representation (vectors) and how to find interesting groups of documents using hierarchical clustering. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 13044 Orange Data Mining
Mining Unstructured Healthcare Data
Deep Dhillon, former Chief Data Scientist at Alliance Health Networks (now at http://www.xyonix.com), presents a talk titled "Mining Unstructured Healthcare Data" to computational linguistics students at the University of Washington on May 8, 2013. Every day doctors, researchers and health care professionals publish their latest medical findings continuously adding to the world's formalized medical knowledge represented by a corpus of millions of peer reviewed research studies. Meanwhile, millions of patients suffering from various conditions, communicate with one another in online discussion forums across the web; they seek both social comfort and knowledge. Medify analyzes the unstructured text of these health care professionals and patients by performing a deep NLP based statistical and lexical rule based relation extraction ultimately culminating in a large, searchable index powering a rapidly growing site trafficked by doctors, health care professionals, and advanced patients. We discuss the system at a high level, demonstrate key functionality, and explore what it means to develop a system like this in the confines of a start up. In addition, we dive into details like ground truth gathering, efficacy assessment, model approaches, feature engineering, anaphora resolution and more. Need a custom machine learning solution like this one? Visit http://www.xyonix.com.
Views: 3691 zang0
Excel spreadsheet with macros for (super quick) categorizing of data.
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: 900 Michael Dew
SAXually Explicit Images: Data Mining Large Shape Databases
Google TechTalks May 12, 2006 Eamonn Keogh ABSTRACT The problem of indexing large collections of time series and images has received much attention in the last decade, however we argue that there is potentially great untapped utility in data mining such collections. Consider the following two concrete examples of problems in data mining. Motif Discovery (duplication detection): Given a large repository of time series or images, find approximately repeated patterns/images. Discord Discovery: Given a large repository of time series or images, find the most unusual time series/image. As we will show, both these problems have applications in fields as diverse as anthropology, crime...
Views: 4638 Google
Lecture 24 —  Community Detection in Graphs - Motivation | Stanford University
. 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. .
Support Vector Machine - Georgia Tech - Machine Learning
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: 137328 Udacity
Statistical Aspects of Data Mining (Stats 202) Day 1
Google Tech Talks June 26, 2007 ABSTRACT This is the Google campus version of Stats 202 which is being taught at Stanford this summer. I will follow the material from the Stanford class very closely. That material can be found at www.stats202.com. The main topics are exploring and visualizing data, association analysis, classification, and clustering. The textbook is Introduction to Data Mining by Tan, Steinbach and Kumar. Googlers are welcome to attend any classes which they think might be of interest to them. Credits: Speaker:David Mease
Views: 213570 GoogleTechTalks
Import Data and Analyze with MATLAB
Data are frequently available in text file format. This tutorial reviews how to import data, create trends and custom calculations, and then export the data in text file format from MATLAB. Source code is available from http://apmonitor.com/che263/uploads/Main/matlab_data_analysis.zip
Views: 343364 APMonitor.com
iOS Swift Tutorial: Guide to Using JSON Data from the Web
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: 42393 Brian Advent
QDA Miner - Creating a Project from a List of Documents
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.
How kNN algorithm works
In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 360049 Thales Sehn Körting
Kaggle Live-Coding: Named Entity Recognition
Join Kaggle data scientist Rachael live as she works on data science projects! See all previous live-coding streams here: https://www.youtube.com/watch?v=i92VI289zWw&list=PLqFaTIg4myu9f21aM1POYVeoaHbFf1hMc
Views: 777 Kaggle
Mod-01 Lec-05 Sequence Labelling and Noisy Channel
Natural Language Processing by Prof. Pushpak Bhattacharyya, Department of Computer science & Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 4631 nptelhrd
#bbuzz 17: Doug Turnbull & Jason Kowalewski – We built an Elasticsearch Learning to Rank plugin
Futher information: https://berlinbuzzwords.de/17/session/we-built-elasticsearch-learning-rank-plugin-then-came-hard-part Learning to Rank uses machine learning to improve the relevance of search results. In this talk, I discuss how we built a learning to rank plugin for Elasticsearch. But what's more interesting is what happened next. Learning to rank requires new ways of thinking about search relevance, and in this talk I go on to discuss the specific problems faced by production-ready learning to rank systems. We learned these hard way so you don't have to. These systems need to solve a variety of problems including: Correctly measuring, using analytics, what a user deems ""relevant"" or ""irrelevant"" Hypothesizing which features of users, queries, or documents (or query-user dependent features) might correlate to relevance Logging/Gathering hypothesized features using the search engine Training models in a scalable fashion Selecting and evaluate models for appropriateness and minimal error Integrating models in a live search system alongside business logic, and other non-relevance considerations A/B testing learning to rank models and avoiding future bias of training data Each of these requires solving pretty tough problems. This talk will discuss our war stories, practical lessons, and the goings-on inside real life search implementations that can help you decide what pitfalls to avoid and decide whether learning to rank is the right direction for your search problem. Speaker: Doug Turnbull & Jason Kowalewski
Beating DFARS 7012 with Data Discovery and Classification
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: 122 Spirion
Text By the Bay 2015: Jeff Sukharev, Machine Translation Approach for Name Matching in Record Link
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: 217 FunctionalTV
Dognition Recommendation Coursera
This video is the final project deliverable of Data visualization with tableau course at Coursera.
Views: 1268 Neha Tyagi
Building Search Strategies - Selected text analysis tools: part 1
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
WordStat - Topic Extraction
In this video, we are going to show you how you can extract topics automatically with WordStat - Content Analysis and Text Mining Software.
QDA Miner - Merging Qualitative and Quantitative Data Files
This tutorial shows how to mix qualitative or unstructured textual data with quantitative information stored separately in QDA Miner.
Towards Contextual Text Mining
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: 73 Microsoft Research
Named entity recognition (NER) labeling tool
https://dataturks.com/features/document-ner-annotation.php Allows annotations on full length PDFs, Docs, Docx etc. Supports overlapping annotation. Export data in Json, Stanford NLP or spacy formats.
Views: 20 Dataturks Videos
Machine learning models + IoT data = a smarter world (Google I/O '18)
With the IoT market set to triple in size by 2020, and massive increases in computing power on small devices, the intersection of IoT and machine learning is a trend that all developers should pay attention to. This talk will cover three core use cases, including: how to manage sourcing data from IoT devices to drive machine-learned models; how to deploy and use trained models on mobile devices; and how to do on-device training with a Raspberry Pi computer. Rate this session by signing-in on the I/O website here → https://goo.gl/rYcGev Watch more IoT sessions from I/O '18 here → https://goo.gl/xfowJ8 See all the sessions from Google I/O '18 here → https://goo.gl/q1Tr8x Subscribe to the Google Developers channel → http://goo.gl/mQyv5L #io18
Views: 17584 Google Developers
Belajar Data Mining - Algoritma Decision Tree C4.5
Algoritma C4.5 adalah salah satu metode pada Decision Tree / Pohon Keputusan yang banyak dimanfaatkan untuk melakukan prediksi terhadap suatu kasus. Selamat Belajar, Jangan lupa untuk subscribe, like dan share Terima kasih atas support kalian!
Views: 7118 Wong AiTi
Naive Bayes for Text Classification - Part 2/3
This is PART 2 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 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: 92 Abhishek Babuji
What Is Tree Pruning In Data Mining?
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: 290 Evelina Hornak Tipz
Intrusion Detection System Using Data Mining Technique Support Vector Machine.
Sai infocorp Solution Pvt. Ltd. Mobile no- 9028924212. Address: Head office: 2nd floor, Yashomandir Avenue, Behind Maleria Bus Stop, Patil lane No. 1, College Road, nashik- 422005. Phone no- 0253 6644344. Mobile no- 9028924212. Branch office: Office No-307, 3rd Floor, Om Chembars, Above Hotel Panchali, JM Road, Shivaji nagar, pune- 411005 Phone no- 020 30107071. Email: [email protected] Website: www.saiinfosolution.co.in Read more projects: http://saiinfosolution.co.in/project.jsp
ForceSPIRE : Using Semantic Interaction for Visual Text Analytics
ForceSpire enables text analytics by combining the massive-data foraging abilities of statistical models and mining algorithms with the sensemaking abilities of analysts. In ForceSpire, we use dimensionality reduction methods and similarity metrics to visualize textual document collections in a spatial visual metaphor, where similarities between documents are approximately represented through their relative spatial proximities in a 2D layout. This metaphor is designed to mimic analysts' mental models of the document collection and support their analytic processes, such as clustering similar documents together. In ForceSpire analysts spatially interact with such models directly within the visual metaphor using interactions that derive from their analytic process, such as searching, highlighting, annotating, and repositioning documents. For example, analysts express their expert domain knowledge about the documents by simply moving them, which guides the underlying model to improve the overall layout, taking the user's feedback into account. User study results indicate that the model incrementally learns in accordance with the analyst's insight, the visualization appropriately adapts to the analyst's expertise, and the model discovers useful knowledge signatures that help analyst's find relevant information. Authors: Alex Endert Patrick Fiaux Chris North Virginia Tech More information at http://people.cs.vt.edu/aendert
Views: 1058 Alex GT
Efficient retrieval over documents encrypted by attributes on cloud computing - IEEE PROJECTS 2018
Efficient retrieval over documents encrypted by attributes on cloud computing- IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project AI - MACHINE LEARNING (ML) 1. Machine Learning and Deep Learning Methods for Cybersecurity 2. How Data-Driven Entrepreneur Analyzes Imperfect Information for Business Opportunity Evaluation 3. Image Reconstruction Is a New Frontier of Machine Learning 4. Predicting the Top-N popular videos via a cross-domain hybrid model 5. Robust Malware Detection for Internet Of (Battlefield) Things Devices Using Deep Eigenspace Learning 6. Toward Better Statistical Validation of Machine Learning-Based Multimedia Quality Estimators 7. IMAGE BASED APPRAISAL OF REAL ESTATE PROPERTIES 8. Review of the Use of AI Techniques in Serious Games: Decision-Making and Machine 9. Personalized affective feedback to address students’ frustration in ITS 10. A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security 11. Data Analytics Approach to the Cybercrime Underground Economy 12. Achieving Data Truthfulness and Privacy Preservation in Data Markets 13. Designing Cyber Insurance Policies: The Role of Pre-Screening and Security Interdependence 14. Smart bears don't talk to strangers: Analysing privacy concerns and technical solutions in smart toys for children 15. Price-based Resource Allocation for Edge Computing: A Market Equilibrium Approach 16. Data-Driven Design of Fog Computing aided Process Monitoring System for Large-Scale Industrial Processes 17. Fog-Aided Verifiable Privacy Preserving Access Control for Latency-Sensitive Data Sharing in Vehicular Cloud Computing 18. Efficient Vertical Mining of High Average-Utility Itemsets based on Novel Upper-Bounds 19. RTSense: Providing Reliable Trust-Based Crowdsensing Services in CVCC 20. Machine Learning Cryptanalysis of a Quantum Random Number Generator 21. Secure Distributed Computing with Straggling Servers Using Polynomial Codes 22. Assessing the effectiveness of riparian restoration projects using Landsat and precipitation data from the cloud-computing application ClimateEngine.org 23. Auto-Weighted Multi-view Learning for Image Clustering and Semi-supervised Classification 24. Detecting Spammer Groups from Product Reviews: A Partially Supervised Learning Model 25. Evaluating Insider Threat DetectionWorkflow Using Supervised and Unsupervised Learning 26. Leveraging Unlabelled Data for Emotion Recognition with Enhanced Collaborative Semi-Supervised Learning 27. Link Weight Prediction Using Supervised Learning Methods and Its Application to Yelp Layered Network 28. Max-Margin Deep Generative Models for (Semi-)Supervised Learning 29. Multi-Traffic Scene Perception Based on Supervised Learning 30. Predicting Microblog Sentiments via Weakly Supervised Multi-Modal Deep Learning 31. Residential Household Non-Intrusive Load Monitoring via Graph-Based Multi-Label Semi-Supervised Learning 32. Self Paced Deep Learning for Weakly Supervised Object Detection 33. Self-Taught Low-Rank Coding for Visual Learning 34. Semi-Supervised Autoencoders for Speech Emotion Recognition 35. Semi-Supervised Learning Through Label Propagation on Geodesics 36. Spectral Learning for Supervised Topic Models 37. Webly-supervised Fine-grained Visual Categorization via Deep Domain Adaptation
Text By the Bay 2015: Joaquin Delgado and Diana Hu, ML Scoring: Where Machine Learning Meets Search
Search can be viewed as a combination of a) A problem of constraint satisfaction, which is the process of finding a solution to a set of constraints (query) that impose conditions that the variables (fields) must satisfy with a resulting object (document) being a solution in the feasible region (result set), plus b) A scoring/ranking problem of assigning values to different alternatives, according to some convenient scale. This ultimately provides a mechanism to sort various alternatives in the result set in order of importance, value or preference. In particular scoring in search has evolved from being a document centric calculation (e.g. TF-IDF) proper from its information retrieval roots, to a function that is more context sensitive (e.g. include geo-distance ranking) or user centric (e.g. takes user parameters for personalization) as well as other factors that depend on the domain and task at hand. However, most system that incorporate machine learning techniques to perform classification or generate scores for these specialized tasks do so as a post retrieval re-ranking function, outside of search! In this talk I show ways of incorporating advanced scoring functions, based on supervised learning and bid scaling models, into popular search engines such as Elastic Search and SOLR. I'll provide practical examples of how to construct such "ML Scoring" plugins in search to generalize the application of a search engine as a model evaluator for supervised learning tasks. This will facilitate the building of systems that can do computational advertising, recommendations and specialized search systems, applicable to many domains. Joaquin A. Delgado, PhD. is currently Director of Advertising and Recommendations at OnCue (acquired by Verizon). Previous to that he held CTO positions at AdBrite, Lending Club and TripleHop Technologies (acquired by Oracle). He was also Director of Engineering and Sr. Architect Principal at Yahoo! His expertise lies on distributed systems, advertising technology, machine learning, recommender systems and search. He holds a Ph.D in computer science and artificial intelligence from Nagoya Institute of Technology, Japan. Diana Hu is exploring the depths through breadth in flow, perception and data. ---------------------------------------------------------------------------------------------------------------------------------------- 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: 450 FunctionalTV
CSCI572 - Information Retrieval and Web Search Engines - Team 31
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
Image labeling by clustering
- Description and Information - This video shows step by step process of labeling hyperspectral image using cluster analysis, renaming and merging of the clusters and training a classifier. -- -- -- -- sdgauss Documentation: http://perclass.com/doc/guide/classifiers/gaussian.html#sdgauss Visit our website: http://perclass.com -- -- -- --
Views: 420 perClassSoftware
Opinion mining from student feedback data using  supervised learning algorithms
Sai infocorp Solution Pvt. Ltd. Mobile no- 9028924212. Address: Head office: 2nd floor, Yashomandir Avenue, Behind Maleria Bus Stop, Patil lane No. 1, College Road, nashik- 422005. Phone no- 0253 6644344. Mobile no- 9028924212. Branch office: Office No-307, 3rd Floor, Om Chembars, Above Hotel Panchali, JM Road, Shivaji nagar, pune- 411005 Phone no- 020 30107071. Email: [email protected] Website: www.saiinfosolution.co.in
Using Categorical Features in Mining Bug Tracking Systems to Assign Bug Reports
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: 10 IJSEA Journal
Sanghamitra Deb | Creating Knowledgebases from unstructured text
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: 1431 PyData
KDD2016 paper 118
Title: Dynamic Clustering of Streaming Short Documents Authors: Shangsong Liang*, University College London Emine Yilmaz, University College London Evangelos Kanoulas, University of Amsterdam Abstract: Clustering technology has found numerous applications in mining textual data. It was shown to enhance the performance of retrieval systems in various different ways, such as identifying different query aspects in search result diversification, improving smoothing in the context of language modeling, matching queries with documents in a latent topic space in ad-hoc retrieval, summarizing documents etc. The vast majority of clustering methods have been developed under the assumption of a static corpus of long (and hence textually rich) documents. Little attention has been given to streaming corpora of short text, which is the predominant type of data in Web 2.0 applications, such as social media, forums, and blogs. In this paper, we consider the problem of dynamically clustering a streaming corpus of short documents. The short length of documents makes the inference of the latent topic distribution challenging, while the temporal dynamics of streams allow topic distributions to change over time. To tackle these two challenges we propose a new dynamic clustering topic model - DCT - that enables tracking the time-varying distributions of topics over documents and words over topics. DCT models temporal dynamics by a short-term or long-term dependency model over sequential data, and overcomes the difficulty of handling short text by assigning a single topic to each short document and using the distributions inferred at a certain point in time as priors for the next inference, allowing the aggregation of information. At the same time, taking a Bayesian approach allows evidence obtained from new streaming documents to change the topic distribution. Our experimental results demonstrate that the proposed clustering algorithm outperforms state-of-the-art dynamic and non-dynamic clustering topic models in terms of perplexity and when integrated in a cluster-based query likelihood model it also outperforms state-of-the-art models in terms of retrieval quality. More on http://www.kdd.org/kdd2016/ KDD2016 Conference will be recorded and published on http://videolectures.net/
Views: 337 KDD2016 video
You need to store and manage Unstructured data in sql server, what approach would you use
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: 2562 TechBrothersIT

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