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Note: Links lead to the DBLP on the Web. Sholom M. Weiss Fred Damerau , Tong Zhang , Sholom M. Weiss, Nitin Indurkhya : Text categorization for a comprehensive time-dependent benchmark. Inf. Process. Manage. 40 (2): 209-221 (2004) Sholom M. Weiss, Stephen J. Buckley , Shubir Kapoor , Søren Damgaard : Knowledge-based data mining. KDD 2003 : 456-461 Sholom M. Weiss, Naval K. Verma : A system for real-time competitive market intelligence. KDD 2002 : 360-365 Fred Damerau , Tong Zhang , Sholom M. Weiss, Nitin Indurkhya : Experiments in high-dimensional text categorization. SIGIR 2002 : 357-358 Sholom M. Weiss, Chidanand Apté : Automated generation of model cases for help-desk applications. IBM Systems Journal 41 (3): 421-427 (2002) Ricardo Vilalta , Chidanand Apté , Joseph L. Hellerstein , Sheng Ma , Sholom M. Weiss: Predictive algorithms in the management of computer systems. IBM Systems Journal 41 (3): 461-474 (2002) Nitin Indurkhya , Sholom M. Weiss: Solving regression problems with rule-based ensemble classifiers. KDD 2001 : 287-292 Nitin Indurkhya , Sholom M. Weiss: Rule-Based Ensemble Solutions for Regression. MLDM 2001 : 62-72 Sholom M. Weiss, Nitin Indurkhya : Lightweight Collaborative Filtering Method for Binary-Encoded Data. PKDD 2001 : 484-491 Se June Hong , Sholom M. Weiss: Advances in predictive models for data mining. Pattern Recognition Letters 22 (1): 55-61 (2001) Ricardo Vilalta , Chidanand Apté , Sholom M. Weiss: Operational Data Analysis: Improved Predictions Using Multi-computer Pattern Detection. DSOM 2000 : 37-46 Sholom M. Weiss, Nitin Indurkhya : Decision-Rule Solutions for Data Mining with Missing Values. IBERAMIA-SBIA 2000 : 1-10 Sholom M. Weiss, Nitin Indurkhya : Lightweight Rule Induction. ICML 2000 : 1135-1142 Sholom M. Weiss, Brian F. White , Chidanand Apté : Leightweight Document Clustering. PKDD 2000 : 665-672 Sholom M. Weiss, Brian F. White , Chidanand Apté , Fred Damerau : Lightweight Document Matching for Help-Desk Applications. IEEE Intelligent Systems 15 (2): 57-61 (2000) Se June Hong , Sholom M. Weiss: Advanced in Predictive Data Mining Methods. MLDM 1999 : 13-20 Nitin Indurkhya , Sholom M. Weiss: Estimating Performance Gains for Voted Decision Trees. Intell. Data Anal. 2 (1-4): 303-310 (1998) Sholom M. Weiss, Nitin Indurkhya : Selecting the Right-Size Model for Prediction. Appl. Intell. 6 (4): 261-273 (1996) Raguram Sasisekharan , V. Seshadri , Sholom M. Weiss: Data Mining and Forecasting in Large-Scale Telecommunication Networks. IEEE Expert 11 (1): 37-43 (1996) Nitin Indurkhya , Sholom M. Weiss: Using Case Data to Improve on Rule-based Function Approximation. ICCBR 1995 : 217-228 V. Seshadri , Raguram Sasisekharan , Sholom M. Weiss: Feature Extraction for Massive Data Mining. KDD 1995 : 258-262 Sholom M. Weiss, Nitin Indurkhya : Rule-based Machine Learning Methods for Functional Prediction. JAIR 3 : 383-403 (1995) Sholom M. Weiss, Nitin Indurkhya : Decision Tree Pruning: Biased or Optimal? AAAI 1994 : 626-632 Sholom M. Weiss, Nitin Indurkhya : Small Sample Decision tree Pruning. ICML 1994 : 335-342 Raguram Sasisekharan , V. Seshadri , Sholom M. Weiss: Proactive Network Maintenance Using Machine Learning. KDD Workshop 1994 : 453-462 Chidanand Apté , Fred Damerau , Sholom M. Weiss: Towards Language Independent Automated Learning of Text Categorisation Models. SIGIR 1994 : 23-30 Chidanand Apté , Fred Damerau , Sholom M. Weiss: Automated Learning of Decision Rules for Text Categorization. ACM Trans. Inf. Syst. 12 (3): 233-251 (1994) Sholom M. Weiss, Nitin Indurkhya : Rule-Based Regression. IJCAI 1993 : 1072-1078 Sholom M. Weiss, Dawn M. Cohen , Nitin Indurkhya : Transmembrane Segment Prediction from Protein Sequence Data. ISMB 1993 : 420-428 Sholom M. Weiss, Nitin Indurkhya : Optimized Rule Induction. IEEE Expert 8 (6): 61-69 (1993) Sholom M. Weiss, Nitin Indurkhya : Reduced Complexity Rule Induction. IJCAI 1991 : 678-684 Sholom M. Weiss: Small Sample Error Rate Estimation for k-NN Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 13 (3): 285-289 (1991) Sholom M. Weiss, Casimir A. Kulikowski : Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems Morgan Kaufmann 1990 Sholom M. Weiss, Robert S. Galen , Prasad Tadepalli : Maximizing the Predictive Value of Production Rules. Artif. Intell. 45 (1-2): 47-71 (1990) Sholom M. Weiss, Ioannis Kapouleas : An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods. IJCAI 1989 : 781-787 Allen Ginsberg , Sholom M. Weiss, Peter Politakis : Automatic Knowledge Base Refinement for Classification Systems. Artif. Intell. 35 (2): 197-226 (1988) Sholom M. Weiss, Robert S. Galen , Prasad Tadepalli : Optimizing the Predictive Value of Diagnostic Decision Rules. AAAI 1987 : 521-527 Allen Ginsberg , Sholom M. Weiss, Peter Politakis : SEEK2: A Generalized Approach to Automatic Knowledge Base Refinement. IJCAI 1985 : 367-374 Peter Politakis , Sholom M. Weiss: Using Empirical Analysis to Refine Expert System Knowledge Bases. Artif. Intell. 22 (1): 23-48 (1984) Sholom M. Weiss, Casimir A. Kulikowski , Chidanand Apté , Michael Uschold , Jay Patchett , Robert Brigham , Belynda Spitzer : Building Expert Systems for Controlling Complex Programs. AAAI 1982 : 322-326 Sholom M. Weiss, Casimir A. Kulikowski , Saul Amarel , Aran Safir : A Model-Based Method for Computer-Aided Medical Decision-Making. Artif. Intell. 11 (1-2): 145-172 (1978) Sholom M. Weiss, Casimir A. Kulikowski , Aran Safir : A Model-Based Consultation System for the Long-Term Management of Glaucoma. IJCAI 1977 : 826-832 C. A. Kulinowski , Sholom M. Weiss, M. Trigoboff , Aran Safir : Clinical Consultation of Disease Processes: Some A. I. Problems. AISB (ECAI) 1976 : 166-174 1 [ 3 ] 2 [ 4 ] [ 17 ] [ 18 ] [ 29 ] [ 30 ] [ 33 ] [ 38 ] [ 39 ] 3 [ 4 ] 4 [ 42 ] 5 [ 15 ] 6 [ 17 ] [ 18 ] [ 29 ] [ 40 ] [ 43 ] 7 [ 42 ] 8 [ 7 ] [ 10 ] 9 [ 6 ] [ 8 ] 10 [ 38 ] 11 [ 28 ] [ 34 ] 12 [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 20 ] [ 21 ] [ 22 ] [ 24 ] [ 26 ] [ 27 ] [ 31 ] [ 32 ] [ 35 ] [ 36 ] [ 37 ] [ 40 ] [ 43 ] 13 [ 42 ] 14 [ 9 ] 15 [ 2 ] [ 3 ] [ 4 ] [ 11 ] 16 [ 1 ] 17 [ 38 ] 18 [ 4 ] 19 [ 5 ] [ 6 ] [ 8 ] 20 [ 1 ] [ 2 ] [ 3 ] 21 [ 19 ] [ 23 ] [ 25 ] 22 [ 19 ] [ 23 ] [ 25 ] 23 [ 4 ] 24 [ 7 ] [ 10 ] 25 [ 1 ] 26 [ 4 ] 27 [ 41 ] 28 [ 33 ] [ 38 ] 29 [ 29 ] [ 30 ] 30 [ 40 ] [ 43 ]  ©2004 Association for Computing Machinery |
