Readings in Machine Learning

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