| 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 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 1985 |
Dietterich TG, Michalski RS. Discovering patterns in sequences of events Artificial Intelligence. 25: 187-232. DOI: 10.1016/0004-3702(85)90003-7 |
0.292 |
|
| 2004 |
Domanski PA, Yashar D, Kaufman KA, Michalski RS. An optimized design of finned-tube evaporators using the learnable evolution model Hvac and R Research. 10: 201-211. DOI: 10.1080/10789669.2004.10391099 |
0.288 |
|
| 1993 |
Imam IF, Michalski RS. Learning decision trees from decision rules: A method and initial results from a comparative study Journal of Intelligent Information Systems. 2: 279-304. DOI: 10.1007/BF00962072 |
0.288 |
|
| 2000 |
Michalski RS, Cervone G, Kaufman K. Speeding up evolution through learning: LEM Advances in Soft Computing. 4: 243-256. DOI: 10.1007/978-3-7908-1846-8_22 |
0.286 |
|
| 2006 |
Wojtusiak J, Michalski RS. The use of compound attributes in AQ learning Advances in Soft Computing. 35: 189-198. DOI: 10.1007/3-540-33521-8_19 |
0.279 |
|
| 2006 |
Michalski RS, Kaufman KA, Pietrzykowski J, Śniezyński B, Wojtusiak J. Learning symbolic user models for intrusion detection: A method and initial results Advances in Soft Computing. 35: 273-285. DOI: 10.1007/3-540-33521-8_27 |
0.269 |
|
| 2000 |
Simon H, Bibel W, Bundy A, Berliner H, Feigenbaum E, Buchanan B, Selfridge O, Michie D, Nilsson N, Sloman A, Waltz D, Brooks R, Davis R, Shrobe H, Boden M, ... Michalski R, et al. AI's greatest trends and controversies Ieee Intelligent Systems. 15: 8-17. DOI: 10.1109/5254.820322 |
0.268 |
|
| 1997 |
Zhang Q, Duric Z, Michalski RS. Detecting targets in SAR images: A machine learning approach Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1351: 305-312. DOI: 10.1007/3-540-63930-6_135 |
0.225 |
|
| 2004 |
Kaufman KA, Michalski RS. From Data Mining to Knowledge Mining Handbook of Statistics. 24: 47-75. DOI: 10.1016/S0169-7161(04)24002-0 |
0.224 |
|
| 1983 |
Michalski RS. A Computer-Based Advisory System for Diagnosing Soybean Diseases in Illinois Plant Disease. 67: 459. DOI: 10.1094/Pd-67-459 |
0.218 |
|
| 1985 |
Michalski RS, Stepp R. REVEALING CONCEPTUAL STRUCTURE IN DATA BY INDUCTIVE INFERENCE Machine Intelligence. 10: 173-196. |
0.209 |
|
| 1989 |
Fermanian TW, Michalski RS. WEEDER: An Advisory System for the Identification of Grasses in Turf Agronomy Journal. 81: 312-316. DOI: 10.2134/Agronj1989.00021962008100020034X |
0.208 |
|
| 1983 |
Michalski RS, Stepp RE. Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy Ieee Transactions On Pattern Analysis and Machine Intelligence. 396-410. DOI: 10.1109/TPAMI.1983.4767409 |
0.203 |
|
| 1976 |
Michalski RS. PROBLEMS OF DESIGNING AN INFERENTIAL MEDICAL CONSULTING SYSTEM . 151-157. |
0.194 |
|
| 1982 |
Michalski RS, Baskin AB, Spackman KA. LOGIC-BASED APPROACH TO CONCEPTUAL DATABASE ANALYSIS Proceedings - Annual Symposium On Computer Applications in Medical Care. 792-796. |
0.174 |
|
| 1973 |
Michalski RS. AQVAL/1 - COMPUTER IMPLEMENTATION OF A VARIABLE-VALUED LOGIC SYSTEM VL//1 AND EXAMPLES OF ITS APPLICATION TO PATTERN RECOGNITION . 3-17. |
0.167 |
|
| 2000 |
Michalski RS. Inductive databases and knowledge scouts Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1805: 2-3. |
0.16 |
|
| 1996 |
Bloedoru E, Michalski RS. The AQ17-DCI system for data-driven constructive induction and its application to the analysis of world economics Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1079: 108-117. |
0.139 |
|
| 2007 |
Michalski RS, Wojtusiak J. Generalizing data in natural language Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4585: 29-39. |
0.132 |
|
| 2006 |
Michalski RS. Optimizing complex systems by intelligent evolution: The LEMd method and case study Bulletin of the Polish Academy of Sciences: Technical Sciences. 54: 505-513. |
0.129 |
|
| 2006 |
Wojtusiak J, Michalski RS. The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems Gecco 2006 - Genetic and Evolutionary Computation Conference. 2: 1281-1288. |
0.105 |
|
| 2006 |
Wojtusiak J, Michalski RS, Kaufman KA, Pietrzykowski J. The AQ21 natural induction program for pattern discovery: Initial version and its novel features Proceedings - International Conference On Tools With Artificial Intelligence, Ictai. 523-526. DOI: 10.1109/ICTAI.2006.109 |
0.104 |
|
| 2007 |
Kaufman KA, Michalski RS, Pietrzykowski J, Wojtusiak J. An integrated multi-task inductive database VINLEN: Initial implementation and early results Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4747: 116-133. |
0.096 |
|
| 2006 |
Michalski RS, Wojtusiak J, Kaufman KA. Intelligent optimization via learnable evolution model Proceedings - International Conference On Tools With Artificial Intelligence, Ictai. 332-335. DOI: 10.1109/ICTAI.2006.69 |
0.09 |
|
| 2007 |
Wojtusiak J, Michalski RS, Simanivanh T, Baranova AV. The natural induction system AQ21 and its application to data describing patients with metabolic syndrome: Initial results Proceedings - 6th International Conference On Machine Learning and Applications, Icmla 2007. 518-523. DOI: 10.1109/ICMLA.2007.107 |
0.089 |
|
| 2015 |
Maćkala K, Michalski R, Stodólka J, Rausavljević N, Čoh M. The Relationship between Selected Motor Ability Determinants and Anthropometric Characteristics in Adolescent Athletes from Various Sport. Collegium Antropologicum. 39: 139-45. PMID 26434022 |
0.078 |
|
| 1982 |
Michalski RS, Davis JH, Bisht VS, Sinclair JB. PLANT/ds: AN EXPERT CONSULTING SYSTEM FOR THE DIAGNOSIS OF SOYBEAN DISEASES . 133-138. |
0.072 |
|
| 2006 |
Seeman WD, Michalski RS. The CLUSTER3 system for goal-oriented conceptual clustering: Method and preliminary results Wit Transactions On Information and Communication Technologies. 37: 81-90. DOI: 10.2495/DATA060091 |
0.07 |
|
| 1993 |
Michalski RS. Introduction Machine Learning. 11: 109-110. DOI: 10.1007/BF00993073 |
0.07 |
|
| 2019 |
Popowczak M, Rokita A, Świerzko K, Szczepan S, Michalski R, Maćkała K. Are Linear Speed and Jumping Ability Determinants of Change of Direction Movements in Young Male Soccer Players? Journal of Sports Science & Medicine. 18: 109-117. PMID 30787658 |
0.054 |
|
| 2015 |
Maćkala K, Michalski R, Čoh M, Rausavljević N. The Relationship between 200 m Performance and Selected Anthropometric Variables and Motor Abilities in Male Sprinters. Collegium Antropologicum. 39: 69-76. PMID 26434013 |
0.048 |
|
| Hide low-probability matches. |