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Foreword. Preface. 1. Learning = Inferencing + Memorizing; R.S. Michalski. 2. Adaptive Inference; A. Segre, C. Elkan, D. Scharstein, G. Gordon, A. Russell. 3. On Integrating Machine Learning with Planning; G.F. DeJong, M.T. Gervasio, S.W. Bennett. 4. The Role of Self-Models in Learning to Plan; G. Collins, L. Birnbaum, B. Krulwich, M. Freed. 5. Learning Flexible Concepts Using A Two-Tiered Representation; R.S. Michalski, F. Bergadano, S. Matwin., J. Zhang. 6. Competition-Based Learning; J.J. Grefenstette, K.A. De Jong, W.M. Spears. 7. Problem Solving via Analogical Retrieval and Analogical Search Control; R. Jones. 8. A View of Computational Learning Theory; L.G. Valiant. 9. The Probably Approximately Correct (PAC) and Other Learning Models; D. Haussler, M. Warmuth. 10. On the Automated Discovery of Scientific Theories; D. Osherson, S. Weinstein. Index.
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Add Foundations of Knowledge Acquisition, The two volumes of Foundations of Knowledge Acquisition document the recent progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. This volume is subtitled Machine Learning, and there is a companion volume subtitled, Foundations of Knowledge Acquisition to the inventory that you are selling on WonderClubX
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Add Foundations of Knowledge Acquisition, The two volumes of Foundations of Knowledge Acquisition document the recent progress of basic research in knowledge acquisition sponsored by the Office of Naval Research. This volume is subtitled Machine Learning, and there is a companion volume subtitled, Foundations of Knowledge Acquisition to your collection on WonderClub |