An Introduction to Computational Learning Theory

DOWNLOAD NOW »

Author: Michael J. Kearns,Umesh Virkumar Vazirani,Umesh Vazirani

Publisher: MIT Press

ISBN: 9780262111935

Category: Computers

Page: 207

View: 5857

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam's Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

Learning Theory and Kernel Machines

16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings

DOWNLOAD NOW »

Author: Bernhard Schoelkopf,Manfred K. Warmuth

Publisher: Springer Science & Business Media

ISBN: 3540407200

Category: Computers

Page: 754

View: 1380

This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.

Linguistic Nativism and the Poverty of the Stimulus

DOWNLOAD NOW »

Author: Alexander Clark,Shalom Lappin

Publisher: John Wiley & Sons

ISBN: 9781444390551

Category: Language Arts & Disciplines

Page: 264

View: 3080

This unique contribution to the ongoing discussion of language acquisition considers the Argument from the Poverty of the Stimulus in language learning in the context of the wider debate over cognitive, computational, and linguistic issues. Critically examines the Argument from the Poverty of the Stimulus - the theory that the linguistic input which children receive is insufficient to explain the rich and rapid development of their knowledge of their first language(s) through general learning mechanisms Focuses on formal learnability properties of the class of natural languages, considered from the perspective of several learning theoretic models The only current book length study of arguments for the poverty of the stimulus which focuses on the computational learning theoretic aspects of the problem

Data Mining Algorithms

Explained Using R

DOWNLOAD NOW »

Author: Pawel Cichosz

Publisher: John Wiley & Sons

ISBN: 1118950801

Category: Mathematics

Page: 720

View: 4329

Data Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.

The Computational Complexity of Machine Learning

DOWNLOAD NOW »

Author: Michael J. Kearns

Publisher: MIT Press

ISBN: 9780262111522

Category: Computers

Page: 165

View: 2556

We also give algorithms for learning powerful concept classes under the uniform distribution, and give equivalences between natural models of efficient learnability. This thesis also includes detailed definitions and motivation for the distribution-free model, a chapter discussing past research in this model and related models, and a short list of important open problems."

Computational Complexity

A Modern Approach

DOWNLOAD NOW »

Author: Sanjeev Arora,Boaz Barak

Publisher: Cambridge University Press

ISBN: 9781139477369

Category: Computers

Page: N.A

View: 386

This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set. The book starts with a broad introduction to the field and progresses to advanced results. Contents include: definition of Turing machines and basic time and space complexity classes, probabilistic algorithms, interactive proofs, cryptography, quantum computation, lower bounds for concrete computational models (decision trees, communication complexity, constant depth, algebraic and monotone circuits, proof complexity), average-case complexity and hardness amplification, derandomization and pseudorandom constructions, and the PCP theorem.

Computational Learning Theory

Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings

DOWNLOAD NOW »

Author: Shai Ben-David

Publisher: Springer Science & Business Media

ISBN: 9783540626855

Category: Computers

Page: 330

View: 9160

Content Description #Includes bibliographical references and index.

Computational Learning Theory

DOWNLOAD NOW »

Author: M. H. G. Anthony,N. Biggs

Publisher: Cambridge University Press

ISBN: 9780521599221

Category: Computers

Page: 157

View: 6866

This an introduction to the theory of computational learning.