Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting.
Author: Michael J. Kearns
Publisher: MIT Press
ISBN: 0262111934
Category: Computers
Page: 207
View: 388
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.
An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.
Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262012111
Category: Computers
Page: 415
View: 818
An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.
Learning to Classify Text Using Support Vector Machines - Methods , Theory ,
and Algorithms . Kluwer , Dordrecht ... MIT Press , Cambridge , MA , 1999 . M.
Kääriäinen . ... In Proceedings of the Annual Conference on Computational
Learning Theory , 2005 . ... An Introduction to Computational Learning Theory . MIT Press ...
Author: Olivier Chapelle
Publisher: Mit Press
ISBN: 9780262514125
Category: Computers
Page: 508
View: 898
In the field of machine learning, semi-supervised learning (SSL) occupies the middleground, between supervised learning (in which all training examples are labeled) and unsupervisedlearning (in which no label data are given). Interest in SSL has increased in recent years,particularly because of application domains in which unlabeled data are plentiful, such as images,text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-artalgorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectiveson ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideasunderlying the field: smoothness, cluster or low-density separation, manifold structure, andtransduction. The core of the book is the presentation of SSL methods, organized according toalgorithmic strategies. After an examination of generative models, the book describes algorithmsthat implement the low-density separation assumption, graph-based methods, and algorithms thatperform two-step learning. The book then discusses SSL applications and offers guidelines for SSLpractitioners by analyzing the results of extensive benchmark experiments. Finally, the book looksat interesting directions for SSL research. The book closes with a discussion of the relationshipbetween semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are ResearchScientists and Bernhard Schölkopf is Professor and Director at the Max Planck Institute forBiological Cybernetics in Tübingen. Schölkopf is coauthor of Learning with Kernels (MIT Press, 2002)and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances inLarge-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all publishedby The MIT Press.
Author: Harvard University Center for Research in Computing TechnologyPublish On: 1990
We also give algorithms for learning powerful concept classes under the uniform distribution, and give equivalences between natural models of efficient learnability.
Author: Harvard University Center for Research in Computing Technology
Publisher: MIT Press
ISBN: 0262111527
Category: Computers
Page: 165
View: 219
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."
9. M. Kearns and U. Vazirani. An introduction to computational learning theory. MIT Press, Cambridge, MA, 1994. 10. R. Khardon. On using the Fourier transform
to learn disjoint DNF. Information Processing Letters, 49:219–222, 1994. 11.
Author: Bernhard Schoelkopf
Publisher: Springer Science & Business Media
ISBN: 9783540407201
Category: Computers
Page: 754
View: 855
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.
The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and ...
Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 9780262303262
Category: Computers
Page: 584
View: 139
A new edition of an introductory text in machine learning that gives a unified treatment of machine learning problems and solutions. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. The second edition of Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. In order to present a unified treatment of machine learning problems and solutions, it discusses many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The text covers such topics as supervised learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, and reinforcement learning. New to the second edition are chapters on kernel machines, graphical models, and Bayesian estimation; expanded coverage of statistical tests in a chapter on design and analysis of machine learning experiments; case studies available on the Web (with downloadable results for instructors); and many additional exercises. All chapters have been revised and updated. Introduction to Machine Learning can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.
Efficient Noise - Tolerant Learning from Statistical Queries . In “ 25th Symposium
on Theory of Computation , " pages 392-401 , 1993 . [ 23 ] M. Kearns and U.
Vazirani . An Introduction to Computational Learning Theory . MIT Press ...
[ 28 ] Haussler , D. , Decision theoretic generalizations of the PAC model for
neural net and other learning applications . ... ACM Press , New York , 433–444 ,
1989. ... M. and Vazirani , U. , An Introduction to Computational Learning Theory .
Author: Mikhail J. Atallah
Publisher: CRC Press
ISBN: 142004950X
Category: Computers
Page: 1312
View: 693
Algorithms and Theory of Computation Handbook is a comprehensive collection of algorithms and data structures that also covers many theoretical issues. It offers a balanced perspective that reflects the needs of practitioners, including emphasis on applications within discussions on theoretical issues. Chapters include information on finite precision issues as well as discussion of specific algorithms where algorithmic techniques are of special importance, including graph drawing, robotics, forming a VLSI chip, vision and image processing, data compression, and cryptography. The book also presents some advanced topics in combinatorial optimization and parallel/distributed computing. · applications areas where algorithms and data structuring techniques are of special importance · graph drawing · robot algorithms · VLSI layout · vision and image processing algorithms · scheduling · electronic cash · data compression · dynamic graph algorithms · on-line algorithms · multidimensional data structures · cryptography · advanced topics in combinatorial optimization and parallel/distributed computing
( Gav03 ] D . Gavinsky , Optimally - smooth adaptive boosting and application to
agnostic learning , Journal of Machine Learning ... ( KV94 ] M . J . Kearns and U .
V . Vazirani , An Introduction to Computational Learning Theory , MIT Press ,
1994 .
Systems that Learn, An Introduction to Learning Theory for Cognitive and
Computer Scientists. MIT Press, Cambridge, Mass., 1986. [Qui92] J. Quinlan. C4-
5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo,
CA, ...
Author: Shai Ben-David
Publisher: Springer Science & Business Media
ISBN: 3540626859
Category: Computers
Page: 330
View: 384
Content Description #Includes bibliographical references and index.
MIT Press. Haussler D 1988 Quantifying inductive bias: AI learning algorithms
and Valiant's learning framework. Artificial Intelligence 36, 177–221. Kearns M
and Vazirani U 1994 An Introduction to Computational Learning Theory. MIT Press.
Author: Pawel Cichosz
Publisher: John Wiley & Sons
ISBN: 9781118950807
Category: Mathematics
Page: 720
View: 934
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.
To appear in the Proceedings of Twelfth International Conference on Machine
Learning , Taos , July 1995 . ( KV94 ] M. J. Kearns and U. V. Vazirani . An Introduction to Computational Learning Theory . MIT Press , Cambridge
Massachusetts ...
This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.
Author: Richard S. Sutton
Publisher: A Bradford Book
ISBN: 9780262039246
Category: Computers
Page: 552
View: 321
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students.
Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 9780262325752
Category: Computers
Page: 640
View: 797
A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
Kearns, M. and G. Valiant (1994), Cryptographic limitations on learning boolean
formulae and finite automata. JACM 41 (1), 67-95. Kearns, M.J. and UV. Vazirani
(1994), An Introduction to Computational Learning Theory. MIT Press. Kearns ...
Author: Alexander Clark
Publisher: John Wiley & Sons
ISBN: 1444390554
Category: Language Arts & Disciplines
Page: 264
View: 851
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
The MIT Press , 1990 . [ 4 ] A. DeJong , Kenneth . Learning with genetic
algorithms : an overview . Machine Learning , 3 ( 2 ) : 121138 , 1988 . [ 15 ] M.
Kearns and U. Vazirani . An Introduction to Computational Learning Theory . MIT Press ...
Systems that learn : An introduction to learning theory . Second edition .
Cambridge , Mass .: MIT Press . Kanazawa , M. 1998. Learnable classes of
categorial grammars . CSLI Publications , Stanford University . Kapur , S. 1991. Computational ...
Cryptographic limitations on learning Boolean formulae and finite automata. J.
ACM, 41(1):67–95, 1994. Prelim version STOC '89. M. J. Kearns and U. V.
Vazirani. An Introduction to Computational Learning Theory. MIT Press, 1994. S.
Khot ...
Author: Sanjeev Arora
Publisher: Cambridge University Press
ISBN: 1139477366
Category: Computers
Page:
View: 115
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.
