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Comprehensive Bibliography of Machine Learning

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Comprehensive Bibliography of Machine Learning COMPREHENSIVE BIBLIOGRAPHY OF MACHINE LEARNING Paul E. Utgoff Bernard Nudel Rutgers University This chapter consists of a bibliography of the field of machine learning. The sources of the references include bibliographies compiled by Saul Amarel, Dana Angluin, Ryszard S. Michalski, Tom M. Mitchell, Carl Smith, and Bruce G. Buchanan, as well as our own additions. We define fourteen categories to classify the machine learning literature. For each category, there is a list of reference numbers indicating which references belong within that category. In addition, to the left of each reference is a list of category code letters indicating the categories to which the reference belongs. CATEGORIES Category a. Analogy: Investigation of analogical reasoning in problem solving, and the use of analogy as a learning method. {92, 93, 94, 97, 100, 157,274,332,333,374,472,533,563,564, 565} Category b. Background material: General background reading in areas of artifi­ cial intelligence, cognitive science, and related disciplines that lay the framework for many of the machine learning methods. For overviews of work on machine learning in particular, see category o. {3, 4,5, 16,24,29,35,38,40,51,60,61,71,75,84,90, 107, Ill, 119, 129, 131, 132, 142, 148, 149, 153, 158, 160, 161, 166, 170, 171, 175, 176, 179, 180, 185, 205, 234, 237, 239, 252, 261, 270, 289, 292, 294, 320, 329, 330, 331, 334, 355, 358, 359, 362, 363, 369, 374, 391, 392, 393, 396, 397, 398, 400, 403, 413, 416, 429, 430, 441, 447, 448, 450, 463, 464, 467, 473, 486, 513, 523, 524, 525, 526, 544, 545, 546, 547, 559, 568} 512 COMPREHENSIVE BIBLIOGRAPHY OF MACHINE LEARNING Category c. Concept acquisition: Inductive inference of structural descriptions from training examples. {11, 13, 19,28,30,31,34,36,37,38,41,66,69,70,75,77,78,80,83, 96,97,106,114,115,116, II7, 118, 135, 140, 141, 142, 143, 144, 145, 146, 150, 152, 172, 174, 189, 207, 209, 215, 216, 217, 218, 219, 220, 221, 222, 223, 226, 242, 247, 248, 249, 250, 251, 253, 263; 264, 273, 276, 277, 278, 283, 284, 285, 303, 304, 305, 306, 307, 322, 323, 335, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 356, 357, 364, 365, 366,367, 368, 373, 375, 376, 377, 394, 406, 412, 414, 415, 416, 420, 421, 426, 432, 439, 440, 455, 456, 457, 458, 469,475,476,483,503,504,506,511,515,516,517,531, 534, 536, 537,538,539,543,562,566,567,569,571,572} Category d. Discovery and theory formation: Methods and systems that suggest new concepts and explore relationships among them. {68, 106, 187,210,211,240,288, 296, 297, 298, 300, 301, 302, 310, 312, 313, 316, 336, 338, 428, 526, 531, 570} Category e. Education and Teaching: Intelligent Computer Aided Instruction. {72, 73, 74, 87, 88, 112, 113,214,327,457,469,492,493,494,495,496, 497, 498, 502, 533} Category g. Grammatical Inference: Inferring grammars, formal languages, finite state machines, and Turing machines from examples. {I, 18, 20, 42, 43, 50, 52, 53, 54, 63, 89, 103, 107, 120, 121, 123, 125, 126, 127, 138, 147, 162, 163, 165, 181, 183, 185, 186, 188, 190, 191, 192, 232, 245, 246, 250, 271, 275, 326, 328, 410, 411, 479, 505, 532, 535, 550, 551, 557} Category h. Learning in problem-solving and game playing: Includes self­ improving problem solvers, learning of heuristics and production rules, and shifts in problem representation. {5, 6, 14, 15, 21, 22, 23, 32, 34, 35, 49, 51, 64, 67, 69, 76, 77, 78, 79, 80,82,83,84,88,92,97,104, III, 130, 134, 151, 154, 156, 167, 169, 193, 194, 195, 201, 220, 223, 224, 226, 227, 228, 229, 254, 274, 282, 286, 287, 299, 314, 315, 316, 319, 323, 324, 332, 370, 371, 372, 378, 379, 380, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 399, 401, 404, 405, 409, 417, 437, 443, 451, 458, 459, 460, 461, 474, 475, 481, 485, 488, 497, 498, 499, 502, 510, 518, 519, 522, 537, 538, 539, 542, 543, 544, 548, 549} COMPREHENSIVE BIBLIOGRAPHY OF MACHINE LEARNING 513 Category k. Knowledge acquisition for Expert Systems: Acquiring knowledge to improve performance of Expert Systems. {69, 78, 79, 80, 82, 83, 133, 136, 194, 196, 203, 204, 223, 224, 225, 226, 371, 372, 378, 379, 380, 427, 437, 438, 451, 452, 453, 489, 560} Category I. Language learning: Acquiring grammars, vocabularies, and other aspects of natural language. {9, 10, 12, 85, 89, 91, 93, 94, 107, 123, 129, 138, 159, 213, 219, 271, 284, 308,407,445,465,466, 471, 472, 479, 552} Category m. Modeling of cognitive processes: Work in modeling human learn­ ing and inference processes. {8, 9, 10, 11, 12, 13, 14, 15, 21, 23, 26, 61, 67, 72, 73, 74, 75, 93, 97, 102, 104, 128, 134, 159, 171, 227, 241, 242, 249, 250, 308, 309, 321, 331, 384, 385, 386, 387, 388, 389, 390, 399, 443, 447, 448, 449, 450, 465,466,470,471,473,483,497,498,553} Category o. Overview: Summaries and surveys of work on machine learning. {I, 11, 15, 17, 18, 27, 37, 38, 39, 46, 52, 60, 62, 63, 70, 71, 75, 81, 84, 95, 98, 99, 100, 101, 105, 109, 110, 118, 119, 122, 137, 142, 144, 146, 150, 168, 173, 177, 179, 181, 182, 183, 184, 189, 198, 201, 205, 206, 208, 230, 231, 234, 235, 236, 238, 239, 243, 244, 251, 252, 258, 259, 260, 266, 267, 268, 279, 290, 291, 292, 293, 294, 295, 311, 314, 315, 317, 318, 320, 325, 329, 335, 336, 337, 351, 355, 356, 360, 361, 362, 363, 366, 367, 368, 373, 376, 377, 381, 382, 394, 395, 402, 406, 408, 413, 415, 422, 425, 428, 429, 431, 433, 434, 435, 436, 442, 444, 453, 454,467,484,485,487,490,491,500,506,507,508,509, 512, 514, 520, 525, 527, 529, 530, 540, 541, 558, 561} Category p. Procedure learning and automatic programming: Inferring programs, functions, or procedures from input-output pairs, traces, or high-level specifica­ tions. {2, 4, 33, 43, 44, 45, 47, 48, 49, 55, 56, 57, 58, 59, 62, 86, 130, 155, 164, 200, 202, 212, 255, 256, 257, 262, 269, 272, 280, 281, 314, 389, 418, 419, 422, 423, 424, 425, 462, 475, 477, 480, 482, 501, 521, 548, 554, 555, 556} Category q. Clustering: Learning from observation, unsupervised learning, cluster analysis, and acquiring taxonomic classifications. {7, 139, 150, 199,353,354,446, 517} 514 COMPREHENSIVE BIBLIOGRAPHY OF MACHINE LEARNING Category r. Recognition of patterns: Inferring statistical or syntactic descriptions of classes of objects from examples. {25, 36, 65, 66,85, 105, 108, 124, 139, 150, 177, 178, 179, 182, 183, 184, 197, 233, 258, 259, 260, 261, 265, 282, 338, 339, 343, 363, 402, 447, 449,460,461,463,468,470,478,503,504,528,529,530, 541, ~69} REFERENCES go 1. Adleman, L. and Blum, M., "Inductive Inference and Unsolvability", Technical Report, Dept. Electrical Engineering and Computer Science and the Electronics Research Lab., U. Calif. at Berkeley, 1975. P 2. Amarel, S., "On the Automatic Formation of a Computer Program which represents a Theory", Self-Organizing Systems, Yovits, M., Jacobi, G. and Goldstein, G. (Eds.), Spartan Books, Washington, D.C., pp. 107-175, 1962. b 3. Amarel, S., "On Representations of Problems of Reasoning about Actions", Machine Intelligence, University of Edinburgh Press, Vol. 3, 1968. bp 4. Amarel, S., "Representations and Modeling in Problems of Program Formation", Machine Intelligence, University of Edinburgh Press, Vol. 6, 1971. bh 5. Amarel, S., "Problems of Representation in Heuristic Problem Solving; Related Issues in the Development of Expert Systems", Technical Report CBM-TR-118, Depart­ ment of Computer Science, Rutgers University, February 1981, (To be published in Methods of Heuristics, Groner, Groner and Bischof, Eds., Lawrence Erlbaum As­ sociates, Hillsdale, N.J.). h 6. Amarel, S., "Expert Behavior and Problem Representations", Human and Artificial Intelligence, Elithorn, A. and Banerji, R. (Eds.), Erlbaum, 1982. q 7. Anderberg, M. R., Cluster Analysisfor Applications, Academic Press, 1973. m 8. Anderson, J. R. and Bower, G. H., Human Associative Memory, Winston & Sons, Washington, D.C., 1973. 1m 9. Anderson, J. R., Language. Memory. and Thought, Lawrence Erlbaum Associates, Hillsdale, N.J., 1976. 1m 10. Anderson, J. R., "Induction of Augmented Transition Networks", Cognitive Science, Vol. 1, 1977. emo 11. Anderson, J. R., Kline, P. J. and Beasley, C. M., "A General Learning Theory and its Application to Schema Abstraction", The Psychology of Learning and Motivation, Vol. 13, 1979. 1m 12. Anderson, J. R., "A Theory of Language Acquisition Based on General Learning Principles", Proceedings of the Seventh International Joint Conference on Artificial Intelligence, Vancouver, pp. 97-103, August 1981. em 13. Anderson, J. R. (Ed.), Cognitive Skills and their Acquisition, Erlbaum Associates, Hillsdale, N.J., 1981. COMPREHENSIVE BIBLIOGRAPHY OF MACHINE LEARNING 515 hm 14. Anderson, J. R., Greeno, J. G., Kline, P. J. and Neves, D. M., "Acquisition of Problem-Solving Skill", Cognitive Skills and their Acquisition, Anderson, J. R. (Ed.), Lawrence Erlbaum Associates, Hillsdale, N.J., ch. 6, pp. 191-230, 1981. hmo 15. Anderson, J. R., "Acquisition of Proof Skills in Geometry", Machine Learning, Michalski, R. S., Carbonell, J. G. and Mitchell, T. M. (Eels.), Tioga, 1983. b 16. Andrae, J. H., Thinking with the Teachable Machine, Academic Press, 1977. o 17. Andrews, A. M., "Learning Machines", Proceedings of the Symposium on the Mechanization of Thought Processes, H.M.
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