'Vc dimension' presentation slideshows

Predictive Learning from Data

Predictive Learning from Data. LECTURE SET 4 Statistical Learning Theory (VC-theory). Electrical and Computer Engineering. OUTLINE. Objectives Inductive learning problem setting Keep-It-Direct Principle Statistical Learning Theory Applications Measuring the VC-dimension

1.33k views • 105 slides

A Contribution to Reinforcement Learning; Application to Computer Go

A Contribution to Reinforcement Learning; Application to Computer Go. Sylvain Gelly Advisor: Michele Sebag; Co-advisor: Nicolas Bredeche September 25 th , 2007. Reinforcement Learning: General Scheme. An Environment (or Markov Decision Process) : State Action

1.1k views • 95 slides

On Complexity, Sampling, and ? -Nets and ? -Samples. Present by: Shay Houri

On Complexity, Sampling, and ? -Nets and ? -Samples. Present by: Shay Houri. GOALS. We will try to quantify the notion of geometric complexity. We show that one can capture the structure of a distribution/point set by a small subset.

823 views • 75 slides

Support Vector Machines

Support Vector Machines. ???????. Content. Introduction The VC Dimension & Structure Risk Minimization Linear SVM ? The Separable case Linear SVM ? The Non-Separable case Lagrange Multipliers. Support Vector Machines. Introduction. Learning Machines. A machine to learn the mapping

1.14k views • 74 slides

Assoc. Prof. Nguyen Xuan Hoai, HANU IT R&D Center

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608 views • 46 slides

607 views • 45 slides

Functional Link Network

Functional Link Network. Support Vector Machines. Support Vector Machines. support vectors. separator. margin. Support Vector Machines. Support Vector Machines. Support Vector Machines. Support Vector Machines. Support Vector Machines. Support Vector Machines. Support Vector Machines.

655 views • 43 slides

Computational Learning Theory

Computational Learning Theory. Pardis Noorzad Department of Computer Engineering and IT Amirkabir University of Technology Ordibehesht 1390. Introduction. For the analysis of data structures and algorithms and their limits we have: Computational complexity theory

586 views • 38 slides

Randomized k -Coverage Algorithms for Dense Sensor Networks Mohamed Hefeeda, Majid Bagheri

Randomized k -Coverage Algorithms for Dense Sensor Networks Mohamed Hefeeda, Majid Bagheri School of Computing Science, Simon Fraser University, Canada INFOCOM 2007. Outlines. Introduction Problem definition T wo approximation algorithms Randomized k -coverage algorithm (RKC)

421 views • 28 slides

CS 570 Artificial Intelligence Introduction to Statistical Learning Theory

518 views • 27 slides

Support Vector Machine (SVM)

Support Vector Machine (SVM). MUMT611 Beinan Li Music Tech @ McGill 2005-3-17. Content. Related problems in pattern classification VC theory and VC dimension Overview of SVM Application example. Related problems in pattern classification. Small sample-size effect (peaking effect)

480 views • 24 slides

Thursday, 15 March 2007 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 25 of 42. PAC Learning, VC Dimension, and Mistake Bounds. Thursday, 15 March 2007 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org/Courses/Spring-2007/CIS732 Readings: Sections 7.4.1-7.4.3, 7.5.1-7.5.3, Mitchell

378 views • 24 slides

Support Vector Machine (SVM)

Support Vector Machine (SVM). MUMT611 Beinan Li Music Tech @ McGill 2005-3-17. Content. Related problems in pattern classification VC theory and VC dimension Overview of SVM Application example. Related problems in pattern classification. Small sample-size effect (peaking effect)

311 views • 24 slides

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PAC Learning VC Dimension (Most slides by courtesy of Prof . Carlos Guestrin )

PAC Learning VC Dimension (Most slides by courtesy of Prof . Carlos Guestrin ). 10-701/15-781 Machine Learning Fall 2010 Recitation by Leman Akoglu 11/4/10. We only care about the maximum number of points that can be classified exactly!. Other examples of VC dimension.

428 views • 22 slides

Sketched Derivation of error bound using VC-dimension (1)

Sketched Derivation of error bound using VC-dimension (1). Bound our usual PAC expression by the probability that an algorithm has 0 error on the training examples S but high error on a second random sample , using a Chernoff bound:.

437 views • 20 slides

Chapter 7: Model Selection

Chapter 7: Model Selection. Bias Variance Tradeoff Optimism of Training Error Estimates of In-sample Prediction Error BIC VC Dimension Bootstrap.

205 views • 20 slides

Geoffrey Hinton

CSC321: Introduction to Neural Networks and Machine Learning Lecture 23: Linear Support Vector Machines. Geoffrey Hinton. Getting good generalization on big datasets. If we have a big data set that needs a complicated model, the full Bayesian framework is very computationally expensive.

407 views • 18 slides

Informational Complexity Notion of Reduction for Concept Classes

Informational Complexity Notion of Reduction for Concept Classes. Shai Ben-David Cornell University, and Technion Joint work with Ami Litman Technion. Measures of the Informational Complexity of a class. The VC-dimension of the class.

278 views • 14 slides