| Year |
Citation |
Score |
| 2012 |
Michalski RS, Wojtusiak J. Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery Journal of Intelligent Information Systems. 39: 141-166. DOI: 10.1007/s10844-011-0186-z |
0.347 |
|
| 2009 |
Wojtusiak J, Michalski RS, Simanivanh T, Baranova AV. Towards application of rule learning to the meta-analysis of clinical data: an example of the metabolic syndrome. International Journal of Medical Informatics. 78: e104-11. PMID 19464941 DOI: 10.1016/J.Ijmedinf.2009.04.003 |
0.417 |
|
| 2008 |
Wojtusiak J, Michalski RS. Analyzing diaries for analytical relapse prevention using natural induction: a method and preliminary results. Quality Management in Health Care. 17: 80-9. PMID 18204380 DOI: 10.1097/01.Qmh.0000308640.89602.B1 |
0.354 |
|
| 2006 |
Michalski RS, Kaufman KA. Intelligent evolutionary design: A new approach to optimizing complex engineering systems and its application to designing heat exchangers International Journal of Intelligent Systems. 21: 1217-1248. DOI: 10.1002/Int.20182 |
0.4 |
|
| 2004 |
Maloof MA, Michalski RS. Incremental learning with partial instance memory Artificial Intelligence. 154: 95-126. DOI: 10.1016/J.Artint.2003.04.001 |
0.441 |
|
| 2001 |
Michalski RS, Kaufman KA. Learning patterns in noisy data: The AQ approach Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2049: 22-38. DOI: 10.1007/3-540-44673-7_2 |
0.393 |
|
| 2000 |
Michalski RS, Kaufman KA. Building knowledge scouts using KGL metalanguage Fundamenta Informaticae. 41: 433-447. DOI: 10.3233/Fi-2000-41404 |
0.349 |
|
| 2000 |
Michalski RS. Learnable evolution model: evolutionary processes guided by machine learning Machine Learning. 38: 9-40. DOI: 10.1023/A:1007677805582 |
0.451 |
|
| 2000 |
Maloof MA, Michalski RS. Selecting examples for partial memory learning Machine Learning. 41: 27-52. DOI: 10.1023/A:1007661119649 |
0.416 |
|
| 2000 |
Kaufman KA, Michalski RS. Adjustable description quality measure for pattern discovery using the AQ methodology Journal of Intelligent Information Systems. 14: 199-216. |
0.36 |
|
| 1999 |
Michalski RS, Chilausky RL. Knowledge acquisition by encoding expert rules versus computer induction from examples: A case study involving soybean pathology International Journal of Human Computer Studies. 51: 239-263. DOI: 10.1016/S0020-7373(80)80054-X |
0.435 |
|
| 1999 |
Kaufman KA, Michalski RS. Learning from inconsistent and noisy data: The AQ18 approach Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1609: 411-419. DOI: 10.1007/BFb0095128 |
0.39 |
|
| 1999 |
Michalski RS, Chilausky RL. Knowledge acquisition by encoding expert rules versus computer induction from examples International Journal of Human-Computer Studies \/ International Journal of Man-Machine Studies. 51: 239-263. DOI: 10.1006/Ijhc.1979.0308 |
0.438 |
|
| 1998 |
Bloedorn E, Michalski RS. Data-driven constructive induction Ieee Intelligent Systems and Their Applications. 13: 30-36. DOI: 10.1109/5254.671089 |
0.302 |
|
| 1997 |
Michalski RS, Imam IF. On learning decision structures Fundamenta Informaticae. 31: 49-64. DOI: 10.3233/Fi-1997-3115 |
0.381 |
|
| 1997 |
Maloof MA, Michalski RS. Learning symbolic descriptions of shape for object recognition in X-ray images Expert Systems With Applications. 12: 11-20. DOI: 10.1016/S0957-4174(96)00076-0 |
0.464 |
|
| 1997 |
Michalski RS. Seeking knowledge in the deluge of facts Fundamenta Informaticae. 30: 283-297. |
0.348 |
|
| 1996 |
Imam IE, Michalski RS. Learning for decision making: The FRD approach and a comparative study Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1079: 428-437. |
0.347 |
|
| 1995 |
Zhang J, Michalski RS. An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts Machine Learning. 21: 235-267. DOI: 10.1023/A:1022608301842 |
0.528 |
|
| 1995 |
Arciszewski T, Michalski RS, Dybala T. STAR methodology-based learning about construction accidents and their prevention Automation in Construction. 4: 75-85. DOI: 10.1016/0926-5805(94)00035-L |
0.486 |
|
| 1994 |
Arciszewski T, Bloedorn E, Michalski RS, Mustafa M, Wnek J. Machine learning of design rules: Methodology and case study Journal of Computing in Civil Engineering. 8: 286-308. DOI: 10.1061/(Asce)0887-3801(1994)8:3(286) |
0.478 |
|
| 1994 |
Wnek J, Michalski RS. Hypothesis-Driven Constructive Induction in AQ17-HCI: A Method and Experiments Machine Learning. 14: 139-168. DOI: 10.1023/A:1022622132310 |
0.515 |
|
| 1993 |
Michalski RS. Inferential Theory of Learning as a Conceptual Basis for Multistrategy Learning Machine Learning. 11: 111-151. DOI: 10.1007/Bf00993074 |
0.519 |
|
| 1992 |
Bergadano F, Matwin S, Michalski RS, Zhang J. Learning Two-Tiered Descriptions of Flexible Concepts: The POSEIDON System Machine Learning. 8: 5-43. DOI: 10.1023/A:1022682318197 |
0.527 |
|
| 1992 |
Michalski RS, Kerschberg L, Kaufman KA, Ribeiro JS. Mining for knowledge in databases: The INLEN architecture, initial implementation and first results Journal of Intelligent Information Systems. 1: 85-113. DOI: 10.1007/Bf01006415 |
0.488 |
|
| 1991 |
Michalski RS. Searching for knowledge in a world flooded with facts Applied Stochastic Models and Data Analysis. 7: 153-166. DOI: 10.1002/Asm.3150070205 |
0.435 |
|
| 1990 |
Michalski RS. Learning flexible concepts: fundamental ideas and a method based on two-tiered representation Machine Learning. 63-102. DOI: 10.1016/B978-0-08-051055-2.50007-9 |
0.521 |
|
| 1990 |
Michalski RS, Kodratoff Y. Research in machine learning: recent progress, classification of methods, and future directions Machine Learning. 3-30. DOI: 10.1016/B978-0-08-051055-2.50004-3 |
0.499 |
|
| 1989 |
Fermanian TW, Michalski RS, Katz B, Kelly J. AGASSISTANT: An Artificial Intelligence System for Discovering Patterns in Agricultural Knowledge and Creating Diagnostic Advisory Systems Agronomy Journal. 81: 306-312. DOI: 10.2134/Agronj1989.00021962008100020033X |
0.305 |
|
| 1989 |
Collins AM, Michalski RS. The logic of plausible reasoning: A core theory. Cognitive Science. 13: 1-49. DOI: 10.1207/S15516709Cog1301_1 |
0.311 |
|
| 1988 |
Kokar MM, Atnsaklis PJ, DeJong KA, Meyrowitz AL, Meystel A, Michalski RS, Sutton RS. Machine learning in a dynamic world (panel disc.) . 500-507. |
0.327 |
|
| 1987 |
Medin DL, Wattenmaker WD, Michalski RS. Constraints and preferences in inductive learning: An experimental study of human and machine performance Cognitive Science. 11: 299-339. DOI: 10.1016/S0364-0213(87)80009-5 |
0.484 |
|
| 1986 |
Falkenhainer BC, Michalski RS. Integrating Quantitative and Qualitative Discovery: The ABACUS System Machine Learning. 1: 367-401. DOI: 10.1023/A:1022866732136 |
0.459 |
|
| 1986 |
Langley P, Michalski RS. Editorial: Machine Learning and Discovery Machine Learning. 1: 363-366. DOI: 10.1023/A:1022814715297 |
0.42 |
|
| 1986 |
Stepp RE, Michalski RS. Conceptual clustering of structured objects: A goal-oriented approach Artificial Intelligence. 28: 43-69. DOI: 10.1016/0004-3702(86)90030-5 |
0.367 |
|
| 1986 |
Michalski RS, Winston PH. Variable precision logic Artificial Intelligence. 29: 121-146. DOI: 10.1016/0004-3702(86)90016-0 |
0.307 |
|
| 1983 |
Michalski RS, Stepp RE. Automated construction of classifications: conceptual clustering versus numerical taxonomy. Ieee Transactions On Pattern Analysis and Machine Intelligence. 5: 396-410. PMID 21869124 DOI: 10.1109/Tpami.1983.4767409 |
0.313 |
|
| 1983 |
Michalski RS, Baskin AB, Spackman KA. A logic-based approach to conceptual data base analysis. Medical Informatics = Mã©Decine Et Informatique. 8: 187-95. PMID 6600042 DOI: 10.3109/14639238309016082 |
0.302 |
|
| 1983 |
Carbonell JG, Michalski RS, Mitchell TM. Machine Learning: A Historical and Methodological Analysis Ai Magazine. 4: 69-79. DOI: 10.1609/Aimag.V4I3.406 |
0.476 |
|
| 1983 |
Michalski RS. A theory and methodology of inductive learning Artificial Intelligence. 20: 111-161. DOI: 10.1007/978-3-662-12405-5_4 |
0.504 |
|
| 1981 |
Dietterich TG, Michalski RS. Inductive learning of structural descriptions. Evaluation criteria and comparative review of selected methods Artificial Intelligence. 16: 257-294. DOI: 10.1016/0004-3702(81)90002-3 |
0.498 |
|
| 1980 |
Michalski RS. Pattern recognition as rule-guided inductive inference. Ieee Transactions On Pattern Analysis and Machine Intelligence. 2: 349-61. PMID 21868911 DOI: 10.1109/Tpami.1980.4767034 |
0.426 |
|
| 1980 |
Michalski RS. Pattern Recognition as Rule-Guided Inductive Inference Ieee Transactions On Pattern Analysis and Machine Intelligence. 349-361. DOI: 10.1109/TPAMI.1980.4767034 |
0.335 |
|
| 1977 |
Larson J, Michalski RS. Inductive inference of VL decision rules Intelligence\/Sigart Bulletin. 63: 38-44. DOI: 10.1145/1045343.1045369 |
0.364 |
|
| 1974 |
Michalski RS. VARIABLE-VALUED LOGIC: SYSTEM VL//1 . 323-346. |
0.326 |
|
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