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Case-Based Reasoning - AI 101
 
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You can support the making of these videos through the AI and Games Patreon page: http://www.patreon.com/ai_and_games Like us on Facebook: http://www.facebook.com/AIandGames Follows us on Twitter: http://www.twitter.com/AIandGames -- In this AI 101 video we take a moment to explore the rationale, requirements and application of the Case-Based Reasoning technique. How do we use it? Why do we use it? And how does it relate to more traditional aspects of human cognitive behaviour? -- Music in this Video: 'Happy Go Lucky ChipTune' Written and Performed by 'Teknoaxe': http://www.youtube.com/user/teknoaxe http://www.teknoaxe.com http://www.patreon.com/teknoaxe
Views: 10233 AI and Games
What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning
 
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What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning - CASE-BASED REASONING definition - CASE-BASED REASONING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deeply explored in cognitive science. Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process: 1. Retrieve: Given a target problem, retrieve from memory cases relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his new-found procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. If for instance a procedure for plain pancakes is mapped to blueberry pancakes, a decision is made to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization.
Views: 4661 The Audiopedia
Recording Cases to Case-Based Reasoning - Georgia Tech - KBAI: Part 2
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud409/l-2129908564/m-2123078585 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud409 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 3010 Udacity
Case Based Reasoning
 
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Views: 1291 Mabel Ortega
Contrast with Case-Based Reasoning - Georgia Tech - KBAI: Part 4
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud409/l-1934948585/m-1945549504 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud409 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 414 Udacity
Assignment: Case-Based Reasoning - Georgia Tech - KBAI: Part 2
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud409/l-2129908564/m-2141208554 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud409 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 502 Udacity
Critical study on Data Mining with IDSS using RAPID technique for Diabetes
 
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Critical study on Data Mining with IDSS using RAPID technique for Diabetes To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9. Mobile: (0)9952649690, Email: [email protected], Website: http://www.jpinfotech.org In data mining, knowledge is extracted using key elements and concepts after identifying relevant and reliable data. But in the field of health care, researchers are finding it difficult to convert the bio-medical database into knowledge at a rapid pace. The medical data is huge, complex and heterogeneous in nature. Data Mining principles& tools are used in conjunction with health care expert systems to extract inherent relationships among data elements as knowledge. By integrating different data mining concepts with expert systems, a new system called “Integrated Decision Support System” (IDSS) is proposed, which can provide better results compared to existing ones. It converts knowledge into useful format and uses different tools for construction of its architecture. To reduce possible solutions for diabetic diagnosis, Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Web Based Portal Joint Asia Diabetes Evaluation( JADE) programs are integrated with Reliable Access and Probabilistic Inference based on clinical Data (RAPID) in the developed IDSS system to enhance existing systems for fast extraction of knowledge.
Views: 30 jpinfotechprojects
Case based reasoning
 
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Views: 1805 Heny Pratiwi
Sistem Pakar Metode Case Based Reasoning (CBR) - [JAC Art Code]
 
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Sistem Pakar Diagnosis Kerusakan Printer Metode Case Based Reasoning (CBR) Algoritma Nearest Neighbor. - Kontak : Mail ➡ [email protected] Instagram ➡ https://www.instagram.com/juni_127001
Views: 1026 JAC Art Code
[HD] Fabio Sartori - Bankruptcy Forecasting Using Case-Based Reasoning
 
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Presentazione alla Special Track su "AI for Society and Economy" XIII Convegno dell'Associazione Italiana per l'Intelligenza Artificiale http://aiia2014.di.unipi.it/ai4/
Views: 121 AI*IA 2014
data mining in Telecommunication part 3
 
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کەمپینی بە کوردی کردنی زانست لە زانکۆی گەشەپیدانی مرۆیی
Views: 173 shahen uhd
What is LAZY LEARNING? What does LAZY LEARNING mean? LAZY LEARNING meaning & explanation
 
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What is LAZY LEARNING? What does LAZY LEARNING mean? LAZY LEARNING meaning - LAZY LEARNING definition - LAZY LEARNING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In machine learning, lazy learning is a learning method in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries. The main advantage gained in employing a lazy learning method, such as case-based reasoning, is that the target function will be approximated locally, such as in the k-nearest neighbor algorithm. Because the target function is approximated locally for each query to the system, lazy learning systems can simultaneously solve multiple problems and deal successfully with changes in the problem domain. The disadvantages with lazy learning include the large space requirement to store the entire training dataset. Particularly noisy training data increases the case base unnecessarily, because no abstraction is made during the training phase. Another disadvantage is that lazy learning methods are usually slower to evaluate, though this is coupled with a faster training phase. Lazy classifiers are most useful for large datasets with few attributes.
Views: 1073 The Audiopedia
Learning Krislet behavior using Case-based Reasoning  with Random Under Sampling
 
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Due to the imbalanced nature of the actions performed, the expert dashes to the ball most often, turns less often and rarely kicks. However, the kick action is most important action in the game. Therefore, RUS was used to make the dataset more balanced (By undersampling the majority classes). The results didn't show a distinct change from the game where the dataset was not filtered.
Views: 3 gunaratne sacha
Facial Expression Recognition
 
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Facial expression recognition based on datamining and Case-Based Reasoning methods. This is an artifical intelligence technique for knowledge discovery. This approach assess confidence of the provided decision on the facial expression. www.javierorozco.co.uk
Views: 584 Javier Orozco
When Do You Use Machine Learning vs. a Rules Based System?
 
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Soups Ranjan provides examples of applications where machine learning makes sense and when it doesn't, and gives examples from real-world applications in the risk domain (anti-fraud, cyber security, account takeover detection). Soups Ranjan is the Director of Data Science at Coinbase, one the largest bitcoin exchanges in the world, where he manages the Risk & Data Science team. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl More videos from QCon.ai 2018 on InfoQ: https://bit.ly/2rNAT8z The InfoQ Architects' Newsletter is your monthly guide to all the topics, technologies and techniques that every professional or aspiring software architect needs to know about. Over 200,000 software architects, team leads, CTOs are subscribed to it. Sign up here: https://bit.ly/2KqYfrs
Views: 5299 InfoQ
Video Demo Aplikasi Sistem Pakar Metode Case Based Reasoning PHP & MySQL
 
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Video Demo Aplikasi Sistem Pakar Metode Case Based Reasoning PHP & MySQL - Aplikasi ini merupakan aplikasi Sistem Pakar dengan Metode CBR (Case Based Reasoning) - Algoritma yang digunakan adalah K-NN (K-Nearest Neighbor) - Algoritma ini akan menentukan kasus berdasarkan kedekatan jarak antara kasus baru (kasus yang di analisa) dengan kasus lama yang telah ada di database sebelumnya - Aplikasi ini dibuat dengan PHP 5.6 & Mariadb 10.2, kompatibel dengan PHP 7.* dan database MySQL Bukalapak : https://www.bukalapak.com/p/komputer/software-original/n2hu4q-jual-aplikasi-sistem-pakar-metode-case-based-reasoning-php-mysql
Views: 70 Repo Informatika
What is RULE-BASED SYSTEM? What dos RULE-BASED SYSTEM mean? RULE-BASED SYSTEM meaning
 
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What is RULE-BASED SYSTEM? What dos RULE-BASED SYSTEM mean? RULE-BASED SYSTEM meaning - RULE-BASED SYSTEM definition - RULE-BASED SYSTEM explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ In computer science, rule-based systems are used as a way to store and manipulate knowledge to interpret information in a useful way. They are often used in artificial intelligence applications and research. Normally, the term 'rule-based system' is applied to systems involving human-crafted or curated rule sets. Rule-based systems constructed using automatic rule inference, such as rule-based machine learning, are normally excluded from this system type. A classic example of a rule-based system is the domain-specific expert system that uses rules to make deductions or choices. For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game. Rule-based systems can be used to perform lexical analysis to compile or interpret computer programs, or in natural language processing. Rule-based programming attempts to derive execution instructions from a starting set of data and rules. This is a more indirect method than that employed by an imperative programming language, which lists execution steps sequentially. A typical rule-based system has four basic components: 1. A list of rules or rule base, which is a specific type of knowledge base. 2. An inference engine or semantic reasoner, which infers information or takes action based on the interaction of input and the rule base. The interpreter executes a production system program by performing the following match-resolve-act cycle: 2a. Match: In this first phase, the left-hand sides of all productions are matched against the contents of working memory. As a result a conflict set is obtained, which consists of instantiations of all satisfied productions. An instantiation of a production is an ordered list of working memory elements that satisfies the left-hand side of the production. 2b. Conflict-Resolution: In this second phase, one of the production instantiations in the conflict set is chosen for execution. If no productions are satisfied, the interpreter halts. 2c. Act: In this third phase, the actions of the production selected in the conflict-resolution phase are executed. These actions may change the contents of working memory. At the end of this phase, execution returns to the first phase. 3. Temporary working memory. 4. A user interface or other connection to the outside world through which input and output signals are received and sent.
Views: 6934 The Audiopedia
add/remove cases example
 
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Case-Based Reasoning in Hockey.
Views: 60 hinmanj88
AI - Knowledge Based Systems
 
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Video summarising the creation, applications and limitations of knowledge based (expert) systems for A Level Computer Science.
Views: 7986 Yatish Parmar
Implementasi Case Based Reasoning CBR
 
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IMPLEMENTASI CASE BASE REASONING PADA PENYAKIT ANJING STMIK WICIDA SAMARINDA PPT DECISION SUPPORT SYSTEM
Views: 494 heri purnomo
Data Mining: Carvana Lemon Car Prediction using SAS Enterprise Miner
 
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Business Case: To predict if the car purchased at the Auction is a bad buy, using car related and purchase related data. Methods: Logistic regression, Decision Trees, Memory Based Reasoning, Neural Networks using SAS Enterprise Miner.
Views: 1646 Sachin's Tech Corner
Advanced Case-Based Reasoning - Georgia Tech - KBAI: Part 2
 
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Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud409/l-2129908564/m-2123078620 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud409 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 1193 Udacity
Rule Based Systems
 
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Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 9511 nptelhrd
Real-World Big Data and Analytics Case Studies
 
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Emily Plachy, IBM Distinguished Engineer, Author of Analytics Across the Enterprise: How IBM Realizes Value from Big Data and Analytics
Final Year Projects | A Hybrid Recommender System Using RuleBased and Case-Based Reasoning
 
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Final Year Projects | A Hybrid Recommender System Using RuleBased and Case-Based Reasoning More Details: Visit http://clickmyproject.com/a-secure-erasure-codebased-cloud-storage-system-with-secure-data-forwarding-p-128.html Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 2170 Clickmyproject
A Case Based Recommendation Approach for Market Basket Data(IEEE Project 2015 - 2016)
 
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IEEE PROJECTS 2015 1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider. It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training. Dot Net DOTNET Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems Java Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems ECE IEEE Projects 2015 1. Matlab project 2. Ns2 project 3. Embedded project 4. Robotics project Eligibility Final Year students of 1. BSc (C.S) 2. BCA/B.E(C.S) 3. B.Tech IT 4. BE (C.S) 5. MSc (C.S) 6. MSc (IT) 7. MCA 8. MS (IT) 9. ME(ALL) 10. BE(ECE)(EEE)(E&I) TECHNOLOGY USED AND FOR TRAINING IN 1. DOT NET 2. C sharp 3. ASP 4. VB 5. SQL SERVER 6. JAVA 7. J2EE 8. STRINGS 9. ORACLE 10. VB dotNET 11. EMBEDDED 12. MAT LAB 13. LAB VIEW 14. Multi Sim CONTACT US 1 CRORE PROJECTS Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamin Nadu, INDIA - 600 026 Email id: [email protected] website:1croreprojects.com Phone : +91 97518 00789 / +91 72999 51536
Views: 322 1 Crore Projects
Bays Theorem in hindi | solved example | Artificial Intelligence | #26
 
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Bayes' theorem is a formula that describes how to update the probabilities of hypotheses when given evidence. It follows simply from the axioms of conditional probability, but can be used to powerfully reason about a wide range of problems involving belief updates. A real-world application example will be weather forecasting.
Views: 133644 Well Academy
FAQ Answers -1 : Analytics Interview Q&A Discussion | Data Science
 
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In this video I shall discuss ten important and basic interview questions asked in technical round of Analytics or Data Science interviews. In data science interview you will get questions from probability, statistics to machine learning and deep learning and also questions from SAS and R. You will also get questions on big data tools and programming. Contact : [email protected] ANalytics Study Pack : http://analyticsuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 80682 Analytics University
Presentation eXiT - Research group (Control Engineering and Intelligent Systems)
 
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The eXiT group is an interdisciplinary research group of the Institute of Informatics and Applications of the University of Girona involved in national and international research and transfer projects. The main research activity of the group is focused on the application of artificial intelligence principles (data mining and knowledge discovery, qualitative reasoning, case based reasoning, auctions, etc.) and machine learning to support decision-making processes. This research is being conducted in mainly two application domains: medicine/Healthcare and Smart cities/smart grids.
MultiClass Classification ll What Is MultiClass Explained with Examples in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 5241 5 Minutes Engineering
Language Based Reasoning and VisualTools Integration
 
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A robot with active vision, semantic memory and reasoning abilities is watching a human performing an everyday task, and in particular, a manipulation task, over a table. In the upper left part of the screen, you will watch the results of the object and hand segmentation, recognition and tracking modules of the robot. A number of objects are placed on the table, such as a hammer, a plank, a ruler and a nail. The objects have been segmented and recognised as such. The hands of the human are also recognised in the scene and tracked. In the upper right part of the screen, you will watch the results of the visual action recognition tool. Using a depth camera to track human movements in 3D space, the module tracks the movement of the hands at any time, its contact (or no contact) to other objects in the scene and decides on the manipulation action that is being carried out. As the scenes starts to evolve dynamically, the visual tools recognise that the human is performing a gripping action with his left hand; he is gripping the plank. This verbal information is analysed by the language tools as a single movement concept, which comprises inherently, a specific tool, a specific affected object and a certain goal. According to the minimalist grammar of action and based on findings from the neuroscience of action, these three are INHERENT CONSTITUENTS of actions. In other words, substituting any of them leads to a DIFFERENT action. This information is correlated to prior knowledge in the semantic memory. What appears faded on the language-half of the screen, is some information from the semantic memory regarding the affordances of a 'plank'. A plank is used as a tool for boarding up something. Through this prior knowledge the reasoner of the robot predicts that the human might have gripped the plank for boarding up something. This is a predicted 'final' goal that the human might have, given what he has done up to this point. The predicted goal is highlighted in red. The reasoner will keep making new predictions as new information comes in from the visual modules, verifying or changing the previous guesses. As the scene continues evolving, the visual tools recognise that the human grasps with his right hand the nail. This is also analysed by the language tools and correlated to prior knowledge in the semantic memory. Searching through known affordances of nails, the robot is now making new predictions to explain why the human may want to get hold of a nail. It may be the case that he will fasten something with the nail, or clinch with something the nail. Next the visual tools see the human reaching the plank with the nail. This information is also analysed and now the previous predictions are narrowed down to two: the human will either fasten the plank with the nail, or stick the nail on the plank. Given the information up to now, the action sequence parser and the reasoner can jointly reconstruct the action sequence dependency tree: following the rules of the minimalist grammar of action, subsequent in time actions that share a tool or affected object form part of the same sequence. In this case, grasping the nail and then reaching with nail the plank are two actions in which the affected object of the former is the tool of the latter; so one enables the other and form part of the same action sequence. The fact that the reaching action involves the plank as affected object, allows the parser to conclude that the grasping of the plank that was the first action to be executed in time, is also part of the same action sequence, whose final goal is not known yet. However, it is being predicted as being a fasten goal. The scene continues evolving. The human is now pushing the nail on the plank with his right hand. The dialogue between the visual tools and language continues. The information is analysed by the language tools and correlated with information in the semantic memory. The action sequence tree is updated with the new information. As the human is grasping the hammer with his right hand and reaching the nail, the robot reasons that it is probably a beating action that will take place. Indeed, this is what happens next! Now the action sequence tree has been updated with all new developments. The robot realises that the final goal of this sequence is indeed to fasten the plank with the nail. The human places the hammer down and retracts his right hand in resting position. At this point, the scene finishes. The robot has now developed a complete tree out of the whole sequence. All single actions observed were related to one final goal, which the robot using its language and vision abilities has concluded to be the fastening of the plank. It was a prediction that was in its mind long time before the whole scene finishes. The exact same language-vision interaction could be used to assist action recognition and object recognition, by using language-based predictions to constrain the search space for the visual tools.
Views: 286 POETICONeu
Ontology-based workflow extraction from texts using word sense disambiguation
 
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Author: Ahmed Halioui, The Université du Québec à Montréal Abstract: This paper introduces a method for automatic workflow extraction from texts using Process-Oriented Case-Based Reasoning (POCBR). While the current workflow management systems implement mostly different complicated graphical tasks based on advanced distributed solutions (e.g. cloud computing and grid computation), workflow knowledge acquisition from texts using case-based reasoning represents more expressive and semantic cases representations. We propose in this context, an ontology-based workflow extraction framework to acquire processual knowledge from texts. Our methodology extends classic NLP techniques to extract and disambiguate tasks in texts. Using a graph-based representation of workflows and a domain ontology, our extraction process uses a context-based approach to recognize workflow components : data and control flows. We applied our framework in a technical domain in bioinformatics : i.e. phylogenetic analyses. An evaluation based on workflow semantic similarities on a gold standard proves that our approach provides promising results in the process extraction domain. Both data and implementation of our framework are available in : http://labo.bioinfo.uqam.ca/tgrowler. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 90 KDD2017 video
SISTEM PAKAR METODE CBR
 
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Tutorial dan Demo Sistem Pakar Metode Case Based Reasoning (CBR). Download source code www.sirkbanget.com
Views: 868 Menunggu Pagi
COLIBRI Studio
 
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This video shows the features of COLIBRI Studio
Views: 6954 gaia ucm
K nearest neighbor - Lazy learner - Algorithm with example
 
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Here we discuss about K nearest neighbor and also what we meant by lazy learner the we discuss the algorithm with visual example. the url of the python package on k-NN is given below : http://scikit-learn.org/stable/modules/neighbors.html
Multi Perspective Learning ll Influence Diagram Explained with Examples in Hindi
 
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📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 4085 5 Minutes Engineering
Business Analytics | Volume 6| Lazy Learning
 
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In machine learning, lazy learning is a learning method in which generalization beyond the training data is delayed until a query is made to the system, as opposed to in eager learning, where the system tries to generalize the training data before receiving queries.
Views: 775 Tarah Technologies
Machine Learning #52 Minimum Description Length & Exploratory Analysis
 
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Machine Learning #52 Minimum Description Length & Exploratory Analysis Machine Learning Complete Tutorial/Lectures/Course from IIT (nptel) @ https://goo.gl/AurRXm Discrete Mathematics for Computer Science @ https://goo.gl/YJnA4B (IIT Lectures for GATE) Best Programming Courses @ https://goo.gl/MVVDXR Operating Systems Lecture/Tutorials from IIT @ https://goo.gl/GMr3if MATLAB Tutorials @ https://goo.gl/EiPgCF
Views: 1792 Xoviabcs
Data Mining E-Learning Www.CreateSmiths.Com
 
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Data Mining E-Learning at Www.CreateSmiths.Com by Blair Smith
Views: 244 site3e
Song Similarity and Genre Prediction @Applied AI Course/ AI Case Study
 
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for more details please visit this link https://www.appliedaicourse.com/courses/song-similarity-and-genre-classification
Views: 1361 Applied AI Course
Book Recommendation System Based on Filtering and Association Rule Mining
 
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Recommendation systems are widely used to recommend products to the end users that are most appropriate. Online book selling websites now-a-days are competing with eachother by many means.Recommendation system is one of the stronger tools to increase profit and retaining buyer. The book recommendation system must recommend books that are of buyer’s interest. This paper presents book recommendation system based on combined features of content filtering, collaborative filtering and association rule mining.
Views: 1702 Final Year Solutions
ODSC West 2015 | Don Dini - "Machine learning and AI: The difference between knowledge and action"
 
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Abstract: In present discourse, the term “machine learning” is often used interchangeably with “artificial intelligence”. Artificial intelligence has, as a central pursuit, reasoning about action. Organized under this goal are many problems including long-term planning, decision making, and knowledge representation. Though AI and ML are far from the same thing, they share a strong, mutually supporting bond. In this talk I will discuss a few examples of this mutual support. First, I will talk about how domain knowledge revealed via learning techniques makes knowledge representation possible in the face of uncertainty, and often makes AI algorithms tractable. This often takes the form of learning domain information to guide search techniques. Secondly, we will discuss how in many cases, guided action must occur in order to collect data about an environment. In these cases, the first step of data collection involves creating an intelligent agent to act and sample from an environment. Lastly, I will talk about why you, the data scientists, should care about AI, and how it can be helpful in your research and application. Bio: Don M. Dini has been practicing, teaching, and writing about data science for over ten years. He studied computer science and artificial intelligence at University of Illinois at Urbana-Champaign and University of Southern California. While at USC he was a lecturer in computer science and worked on applying AI to various real world problems, such as understanding city populations through simulation, and systems to provide security against unknown attackers, which have since been used at LAX, the US coast guard, among other institutions. Today Don is a data scientist at AT&T where he works on building intelligent systems, creating the next generation of communication networks, and creating models to understand human communication. In his free time, Don is an instructor of Kung Fu, and teaches classes in Palo Alto, CA. He competes in tournaments in Los Angeles and San Francisco.
Views: 399 Open Data Science
Range, variance and standard deviation as measures of dispersion | Khan Academy
 
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Practice this lesson yourself on KhanAcademy.org right now: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1228540 Khan Academy