An introduction to computational learning theory. On using the Fourier transform to learn disjoint DNF. Information Processing Letters, —, Khardon, D. Castro and J. Balcazar , simple PAC learning of simple decision lists , Proc. Kearns and V. A probabilistic theory of pattern recognition.
New York: Springer Verlag; Learning pattern Textbooks are [B. Anthony and N. Vazirani, Introduction to Vazirani An Introduction to Computational Learning Theory.
The MIT Press. Renegar, J. Press, Machine Learning. McGraw-Hill, Nock and O. On learning decision committees. In Proc. Dasarathy, B. Approximate statistical tests for comparing supervised classification learning algorithms.
Hopcroft, J. Introduction to The MIT Press, Text classification using string kernels. In Todd K. Leen, Thomas G. Cambridge, Mass. Polynomial time inductive inference of regular term tree languages from positive data. Skip to content. Kearns Publisher: MIT Press ISBN: Category: Computers Page: View: 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.
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. 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.
This an introduction to the theory of computational learning. The 45 revised full papers together with three articles on open problems presented were carefully reviewed and selected from a total of submissions. The papers are organized in topical sections on: learning to rank, boosting, unlabeled data, multiclass classification, online learning, support vector machines, kernels and embeddings, inductive inference, unsupervised learning, generalization bounds, query learning, attribute efficiency, compression schemes, economics and game theory, separation results for learning models, and survey and prospects on open problems.
The 21 revised full papers presented were selected from a total of 35 submissions; also included are two invited contributions. The book is divided in topical sections on learning from queries and counterexamples, reinforcement learning, online learning and export advice, teaching and learning, inductive inference, and statistical theory of learning and pattern recognition. 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.
Besides 13 revised full papers, the book presents three workshop subgroup reports summarizing the contents of the book as well as the state-of-the-art in the areas of scientific data modelling, supporting interactive database exploration, and visualization related metadata. The volume provides a snapshop of current research in the area and surveys the problems that must be addressed now and in the future towards the integration of database management systems and data visualization.
Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system.
First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided.
Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system.
First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting.
Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory.
All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study.
An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels.
It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity.
This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description or the number of bits of information in it is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity.
This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity relations with Godel's incompleteness result , and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory.
The 26 full papers presented were carefully reviewed and selected from a total of 51 submissions. Also included are three invited papers. The 25 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 50 submissions.
They are dedicated to the theoretical foundations of machine learning. The 26 revised full papers presented together with 5 invited contributions and an introduction were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on learning Boolean functions, boosting and margin-based learning, learning with queries, learning and information extraction, inductive inference, inductive logic programming, language learning, statistical learning, and applications and heuristics.
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic models of learning, tools that crisply classify what is and is not efficiently learnable. After a general introduction to Valiant's PAC paradigm and the important notion of the Vapnik-Chervonenkis dimension, the author explores specific topics such as finite automata and neural networks.
The presentation is intended for a broad audience--the author's ability to motivate and pace discussions for beginners has been praised by reviewers. Each chapter contains numerous examples and exercises, as well as a useful summary of important results. An excellent introduction to the area, suitable either for a first course, or as a component in general machine learning and advanced AI courses. Also an important reference for AI researchers. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics.
There is also considerable interaction with the practical, empirical? The papers in this volume cover a broad range of topics of current research in the?
We have divided the 29 technical, contributed papers in this volume into eight categories corresponding to eight sessions re? Below we give a brief overview of the?
Formal models of automated learning re? Inductive Inference models focus on inde? The 22 revised full papers presented together with three invited papers were carefully reviewed and selected from 39 submissions.
The papers are organized in topical sections on statistical learning, inductive logic programming, inductive inference, complexity, neural networks and other paradigms, support vector machines. Author : Miroslav Kubat Publisher: Springer ISBN: Category: Computers Page: View: Read Now » This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications.
The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines.
One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
Author : Michael M. The 26 revised full papers presented were carefully reviewed and selected from a total of 34 submissions. Also included are three invited papers and an introduction by the volume editors. The papers are organized in sections on inductive logic programming and data mining, inductive inference, learning via queries, prediction algorithms, inductive logic programming, learning formal languages, and miscellaneous.
The 24 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning. Author : Arlindo L. The 24 revised full papers presented were carefully reviewed and selected from 35 submissions. The papers address topics like machine learning, automata, theoretical computer science, computational linguistics, pattern recognition, artificial neural networks, natural language acquisition, computational biology, information retrieval, text processing, and adaptive intelligent agents.
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