Readings in machine learning

著者

• Shavlik, Jude W.
• Dietterich, Thomas Glen

書誌事項

Readings in machine learning

edited by Jude W. Shavlik and Thomas G. Dietterich

（The Morgan Kaufmann series in machine learning）

Morgan Kaufmann Publishers, 1990

• : [pbk]

注記

Includes bibliographies and index

内容説明・目次

内容説明

The ability to learn is a fundamental characteristic of intelligent behavior. Consequently, machine learning has been a focus of artificial intelligence since the beginnings of AI in the 1950s. The 1980s saw tremendous growth in the field, and this growth promises to continue with valuable contributions to science, engineering, and business. Readings in Machine Learning collects the best of the published machine learning literature, including papers that address a wide range of learning tasks, and that introduce a variety of techniques for giving machines the ability to learn. The editors, in cooperation with a group of expert referees, have chosen important papers that empirically study, theoretically analyze, or psychologically justify machine learning algorithms. The papers are grouped into a dozen categories, each of which is introduced by the editors.

目次

• Readings in Machine Learning
• Edited by Jude W. Shavlik and Thomas G. Dietterich
• Chapter 1 General Aspects of Machine Learning
• 1.1 Introduction
• 1.1.1 Learning at the Knowledge Level, by T.G. Dietterich
• 1.1.2 Problem Solving and Rule Induction: A Unified View, by H.A. Simon and G. Lea
• 1.1.3 Machine Learning as an Experimental Science, by D. Kibler and P. Langley
• Chapter 2 Inductive Learning From Preclassified Training Examples
• 2.1 Introduction
• 2.2 Algorithms
• 2.2.1 Induction of Decision Trees, by J.R. Quinlan
• 2.2.2 A Theory and Methodology of Inductive Learning, by R.S. Michalski
• 2.2.3 Generalization as Search, by T.M. Mitchell
• 2.2.4 Learning Representative Exemplars of Concepts: An Initial Case Study, by D. Kibler and D.W. Aha
• 2.2.5 Learning Internal Representations by Error Propogation, by D.E. Rumelhart, G.E. Hinton, and R.J. Williams
• 2.2.6 The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, by F. Rosenblatt
• 2.2.7 A Time-Delay Neural Network Architecture for Isolated Word Recognition, by K.J. Lang, A.H. Waibel, and G.E. Hinton
• 2.3 Empirical Comparison
• 2.3.1 An Experimental Comparison of Symbolic and Connectionist Learning Algorithms, by R. Mooney, J. Shavlik, G. Towell, and A. Grove
• 2.3.2 An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods, by S.M. Weiss and I. Kapouleas
• 2.4 Theory
• 2.4.1 The Need for Biases in Learning Generalizations, by T.M. Mitchell
• 2.4.2 A Theory of the Learnable, by L.G. Valiant
• 2.4.3 Occom's Razor, by A. Blumer, A. Ehrenfeucht, D. Haussler, and M.K. Warmuth
• 2.4.4 Qualtifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework, by D. Haussler
• 2.4.5. Learning, by M. Minsky and S.A. Papert
• 2.4.6 On the Complexity of Loading Shallow Neural Networks, by S. Judd
• 2.4.7 What Size Net Gives Valid Generalization?, by E.B. Baum and D. Haussler
• Chapter 3 Unsupervised Concept Learning and Discovery
• 3.1 Introduction
• 3.2 Clustering
• 3.2.1 Knowledge Acquisition Via Incremetnal Conceptual Clustering, by D.H. Fisher
• 3.2.2 The Simulation of Verbal Learning Behavior, by E.A. Feigenbaum
• 3.2.3. AutoClass: A Bayesian Classification System, by P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman
• 3.2.4 Feature Discovery by Competitive Learning, by D.E. Rumelhart and D. Zipser
• 3.2.5 Self-Organized Formation of Topologically Correct Feature Maps
• 3.3 Discovery
• 3.3.1 The Ubiquity of Discovery, by D.B. Lenat
• 3.3.2 Heuristics for Empirical Discovery, by P. Langley, H.A. Simon, and G.L. Bradshaw
• 3.3.3 A Unified Approach to Explanation and Theory Formation, B. Falkenhainer
• 3.3.4 Classifier Systems and Genetic Algorithms, by L.B. Booker, D.E. Goldberg, and J.H. Holland
• Chapter 4 Improving the Efficiency of a Problem Solver
• 4.1 Introduction
• 4.2 Learning Composite Rules
• 4.2.1 Explanation-Based Generalization: A Unifying View, by T.M. Mitchell, R.M. Keller, and S.T. Kedar-Cabelli
• 4.2.2 Explanation-Based Learning: An Alternative View, by G.DeJong and R. Mooney
• 4.2.3 Learning and Executing Generalized Robot Plans, by R.E. Fikes, P.E. Hart, and N.J. Nilsson
• 4.2.4 Acquiring Recursive and Iterative Concepts with Explanation-Based Learning, by J.W. Shavlik
• 4.3 Learning Search Control Knowlege
• 4.3.1 Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics, by T.M. Mitchell, P.E. Utgoff, and R. Banerji
• 4.3.2 Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms, by J.J. Grefenstette
• 4.3.3 Some Studies in Machine Learning Using the Game of Checkers, by A.L. Samuel
• 4.3.4 Chunking in Soar: The Anatomy of a General Learning Mechanism, by J.E. Laird, P.S. Rosenbloom, and A. Newell
• 4.3.5 Quantitative Results Concerning the Utility of Explanation-Based Learning, by S. Minton
• 4.3.6 Defining Operationality for Explanation-Based Learning, by R.M. Keller
• Chapter 5 Using Preexisting Domain Knowledge Inductively
• 5.1 Introduction
• 5.2 Analogical Approaches
• 5.2.1 The Mechanisms of Analogical Learning, by D. Gentner
• 5.2.2 Combining Analogies in Mental Models, by M.H. Burstein
• 5.2.3 Derivational Analgy: A Theory of Reconstructive Problem Solving and Expertise Acquisition, by J.G. Carbonell
• 5.2.4 Toward a Computational Model of Purpose-Directed Analogy, by S. Kedar-Cabelli
• 5.2.5 A Logical Approach to Reasoning by Analogy, by T.R. Davies and S.J. Russell
• 5.2.6 A Theory of the Origins of Human Knowledge, by J.R. Anderson
• 5.3 Cased-Based Approaches
• 5.3.1 Chef, by K.J. Hammond
• 5.3.2 Concept Learning and Heuristic Classification in Weak-Theory Domains, by B.W. Porter, R. Bareiss, and R.C. Holte
• 5.4 Explanatory/Inductive Hybrids
• 5.4.1 Learning One Subprocedure per Lesson, by K. VanLehn
• 5.4.2 Induction of Augmented Transition Networks
• 5.4.3 Learning by Failing to Explain: Using Partial Explanation to Learn in Incomplete and Intractable Domains, by R.J. Hall
• 5.4.4 A Study of Explanation-Based Methods for Inductive Learning, by N.S. Flann and T.G. Dietterich
• 5.4.5 An Approach to Combining Explanation-Based and Neural Learning Algorithms, by J.W. Shavlik and G.G. Towell
• Index
• Credits

「Nielsen BookData」 より

詳細情報

• NII書誌ID(NCID): BA10953857
• ISBN:
  • 1558601430
  • 1558601430
• LCCN: 90006298
• 出版国コード: us
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: San Mateo, Calif.
• ページ数/冊数: x, 853 p.
• 大きさ: 28 cm
• 分類:
  • LCC : Q325.5
  • DC20 : 006.3/1
• 件名:
  • LCSH : Machine learning
• 親書誌ID:
  • BA10842834