'Pac learning' presentation slideshows

Learning

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Machine Learning Algorithms in Computational Learning Theory

Machine Learning Algorithms in Computational Learning Theory. TIAN HE JI GUAN WANG. Shangxuan Xiangnan Kun Peiyong Hancheng. 25 th Jan 2013. Outlines. Introduction Probably Approximately Correct Framework (PAC) PAC Framework Weak PAC-Learnability Error Reduction

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Probably Approximately Correct Learning (PAC)

Probably Approximately Correct Learning (PAC). Leslie G. Valiant. A Theory of the Learnable. Comm. ACM (1984) 1134-1142. Recall: Bayesian learning. Create a model based on some parameters Assume some prior distribution on those parameters Learning problem

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Ensemble Methods for Machine Learning

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Probably Approximately Correct Model (PAC)

Probably Approximately Correct Model (PAC). Example (PAC). Concept: Average body-size person Inputs: for each person: height weight Sample: labeled examples of persons label + : average body-size label - : not average body-size Two dimensional inputs. Example (PAC).

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Homework 1 is due! Homework 2 coming soon

Homework 1 is due! Homework 2 coming soon. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A. CS 154, Lecture 5: The Myhill-Nerode Theorem, On Learning DFAs Streaming Algorithms. TexPoint fonts used in EMF.

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The probably approximately correct (PAC) learning model

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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

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Other Concept Classes

Other Concept Classes. k -term DNF: Disjunctions of at most k unbounded conjunctive terms: ln(| H |)=O( kn ) k -DNF: Disjunctions of any number of terms each limited to at most k literals: ln(| H |)=O( n k ) k -clause CNF: Conjunctions of at most k unbounded disjunctive clauses:

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PAC Learning

PAC Learning. 8/5/2005. purpose. Effort to understand negative selection algorithm from totally different aspects Statistics Machine learning What is machine learning, in a very informal way?

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Computational Learning Theory

Computational Learning Theory. Introduction The PAC Learning Framework Finite Hypothesis Spaces Examples of PAC Learnable Concepts Infinite Hypothesis Spaces Mistake Bound Model of Learning. Sample Complexity for Infinite Hypothesis Spaces.

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Boosting

Thanks to Citeseer and : A Short Introduction to Boosting. Yoav Freund, Robert E. Schapire, Journal of Japanese Society for Artificial Intelligence,14(5):771-780, September, 1999. Boosting. Feb 18, 2008 10-601 Machine Learning. 1936 - T.

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Objectives: Error Bounds Complexity Theory PAC Learning PAC Bound Margin Classifiers

LECTURE 27: PAC-BAYES BOUND. Objectives: Error Bounds Complexity Theory PAC Learning PAC Bound Margin Classifiers Resources: D.M.: Simplified PAC-Bayes J.L.: PAC-Bayes and Margins M.S.: PAC-Bayes and Gaussians Wiki: Rademacher Complexity. Audio:. URL:. Review. Generalized Risk:

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Objectives: Error Bounds Complexity Theory PAC Learning PAC Bound Margin Classifiers

LECTURE 27: PAC-BAYES BOUND. Objectives: Error Bounds Complexity Theory PAC Learning PAC Bound Margin Classifiers Resources: D.M.: Simplified PAC-Bayes J.L.: PAC-Bayes and Margins M.S.: PAC-Bayes and Gaussians Wiki: Rademacher Complexity. Audio:. URL:. Review. Generalized Risk:

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My Wild & Crazy Idea: Causal Computational Learning Theory

My Wild & Crazy Idea: Causal Computational Learning Theory. But first: what ever happened to my WACI from a few years ago: a Web 2.0 mathematics discussion site and conjecture/theorem repository?. Scott Aaronson.

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COLT

COLT. What is learning and Learning from Examples Learning from Examples cont. The Composition Theorem PAC Learning OCCAM Boosting Exact Lea r ning.

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