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Haym Hirsh Vis

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*2008
65EEHaym Hirsh: Data Mining Research: Current Status and Future Opportunities. Statistical Analysis and Data Mining 1(2): 104-107 (2008)
2007
64EESarah Zelikovitz, William W. Cohen, Haym Hirsh: Extending WHIRL with background knowledge for improved text classification. Inf. Retr. 10(1): 35-67 (2007)
2006
63EEAlexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh: Experience-efficient learning in associative bandit problems. ICML 2006: 889-896
2005
62EEAlexander Borgida, Thomas Walsh, Haym Hirsh: Towards Measuring Similarity in Description Logics. Description Logics 2005
61 Sarah Zelikovitz, Haym Hirsh: Improving Text Classification Using EM with Background Text. FLAIRS Conference 2005: 499-505
60 Matthew Stone, Haym Hirsh: Artificial Intelligence: The Next Twenty-Five Years. AI Magazine 26(4): 85-97 (2005)
2004
59EEHaym Hirsh, Nina Mishra, Leonard Pitt: Version spaces and the consistency problem. Artif. Intell. 156(2): 115-138 (2004)
2003
58EESofus A. Macskassy, Haym Hirsh: Adding numbers to text classification. CIKM 2003: 240-246
57 Sarah Zelikovitz, Haym Hirsh: Integrating Background Knowledge Into Text Classification. IJCAI 2003: 1448-1449
56EESofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik: Converting numerical classification into text classification. Artif. Intell. 143(1): 51-77 (2003)
2002
55EESarah Zelikovitz, Haym Hirsh: Integrating Background Knowledge into Nearest-Neighbor Text Classification. ECCBR 2002: 1-5
54 Steve A. Chien, Haym Hirsh: Editorial Introduction: The Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2001). AI Magazine 23(2): 9-10 (2002)
2001
53 Haym Hirsh, Steve A. Chien: Proceedings of the Thirteenth Innovative Applications of Artificial Intelligence Conference, August 7-9, 2001, Seattle, Washington, USA AAAI 2001
52 Sarah Zelikovitz, Haym Hirsh: Using LSI for Text Classification in the Presence of Background Text. CIKM 2001: 113-118
51 Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik: Using Text Classifiers for Numerical Classification. IJCAI 2001: 885-890
50 Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar: Intelligent Information Triage. SIGIR 2001: 318-326
49 Robert S. Engelmore, Haym Hirsh: Editorial Introduction to this Special Issue of AI Magazine: The Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000). AI Magazine 22(2): 13-14 (2001)
48EEChumki Basu, Haym Hirsh, William W. Cohen, Craig G. Nevill-Manning: Technical Paper Recommendation: A Study in Combining Multiple Information Sources. J. Artif. Intell. Res. (JAIR) 14: 231-252 (2001)
2000
47 Gary M. Weiss, Haym Hirsh: A Quantitative Study of Small Disjuncts. AAAI/IAAI 2000: 665-670
46 Khaled Rasheed, Haym Hirsh: Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models. GECCO 2000: 628-635
45 Sarah Zelikovitz, Haym Hirsh: Improving Short-Text Classification using Unlabeled Data for Classification Problems. ICML 2000: 1191-1198
44EEHaym Hirsh, Chumki Basu, Brian D. Davison: Enabling technologies: learning to personalize. Commun. ACM 43(8): 102-106 (2000)
43EEMarti A. Hearst, Haym Hirsh: AI's Greatest Trends and Controversies. IEEE Intelligent Systems 15(1): 8-17 (2000)
1999
42EEDaniel Kudenko, Haym Hirsh: Feature-Based Learners for Description Logics. Description Logics 1999
41EEKhaled Rasheed, Haym Hirsh: Learning to be selective in genetic-algorithm-based design optimization. AI EDAM 13(3): 157-169 (1999)
1998
40 Chumki Basu, Haym Hirsh, William W. Cohen: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. AAAI/IAAI 1998: 714-720
39 Daniel Kudenko, Haym Hirsh: Feature Generation for Sequence Categorization. AAAI/IAAI 1998: 733-738
38 Gary M. Weiss, Haym Hirsh: The Problem with Noise and Small Disjuncts. ICML 1998: 574-
37 William W. Cohen, Haym Hirsh: Joins that Generalize: Text Classification Using WHIRL. KDD 1998: 169-173
36 Sofus A. Macskassy, Arunava Banerjee, Brian D. Davison, Haym Hirsh: Human Performance on Clustering Web Pages: A Preliminary Study. KDD 1998: 264-268
35 Gary M. Weiss, Haym Hirsh: Learning to Predict Rare Events in Event Sequences. KDD 1998: 359-363
34EERonen Feldman, Moshe Fresko, Haym Hirsh, Yonatan Aumann, Orly Liphstat, Yonatan Schler, Martin Rajman: Knowledge Management: A Text Mining Approach. PAKM 1998
33EEMark Schwabacher, Thomas Ellman, Haym Hirsh: Learning to set up numerical optimizations of engineering designs. AI EDAM 12(2): 173-192 (1998)
32 Haym Hirsh: Trends & Controversies: Interactive Fiction. IEEE Intelligent Systems 13(6): 12-21 (1998)
31 Ronen Feldman, Ido Dagan, Haym Hirsh: Mining Text Using Keyword Distributions. J. Intell. Inf. Syst. 10(3): 281-300 (1998)
1997
30 Haym Hirsh, Daniel Kudenko: Representing Sequences in Description Logics. AAAI/IAAI 1997: 384-389
29 Haym Hirsh, Nina Mishra, Leonard Pitt: Version Spaces without Boundary Sets. AAAI/IAAI 1997: 491-496
28 Brian D. Davison, Haym Hirsh: Experiments in UNIX Command Prediction. AAAI/IAAI 1997: 827
27 Haym Hirsh, Brian D. Davison: An Adaptive UNIX Command-Line Assistant. Agents 1997: 542-543
26 Brian D. Davison, Haym Hirsh: Toward an Adaptive Command Line Interface. HCI (2) 1997: 505-508
25 Khaled Rasheed, Haym Hirsh: Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization. ICGA 1997: 513-520
24EEKhaled Rasheed, Haym Hirsh, Andrew Gelsey: A genetic algorithm for continuous design space search. AI in Engineering 11(3): 295-305 (1997)
23 Ronen Feldman, Haym Hirsh: Exploiting Background Information in Knowledge Discovery from Text. J. Intell. Inf. Syst. 9(1): 83-97 (1997)
1996
22 Daniel Kudenko, Haym Hirsh: Representing Sequences in Description Logics Using Suffix Trees. Description Logics 1996: 141-145
21 Ronen Feldman, Haym Hirsh: Mining Associations in Text in the Presence of Background Knowledge. KDD 1996: 343-346
20EEKwong Bor Ng, David Loewenstern, Chumki Basu, Haym Hirsh, Paul B. Kantor: Data Fusion of Machine-Learning Methods for the TREC5 Routing Task (and other work). TREC 1996
1995
19 William W. Cohen, Haym Hirsh: Corrigendum for ``Learnability of Description Logics''. COLT 1995: 463
1994
18 Haym Hirsh, Nathalie Japkowicz: Bootstrapping Training-Data Representations for Inductive Learning: A Case Study in Molecular Biology. AAAI 1994: 639-644
17 William W. Cohen, Haym Hirsh: Learning the Classic Description Logic: Theoretical and Experimental Results. KR 1994: 121-133
16EEHaym Hirsh, Michiel O. Noordewier: Using Background Knowledge to Improve Inductive Learning: A Case Study in Molecular Biology. IEEE Expert 9(5): 3-6 (1994)
15 Haym Hirsh: Generalizing Version Spaces. Machine Learning 17(1): 5-46 (1994)
14 William W. Cohen, Haym Hirsh: The Learnability of Description Logics with Equality Constraints. Machine Learning 17(2-3): 169-199 (1994)
1993
13 Steven W. Norton, Haym Hirsh: Learning DNF Via Probabilistic Evidence Combination. ICML 1993: 220-227
1992
12 Haym Hirsh: Polynomial-Time Learning with Version Spaces. AAAI 1992: 117-122
11 Steven W. Norton, Haym Hirsh: Classifier Learning from Noisy Data as Probabilistic Evidence Combination. AAAI 1992: 141-146
10 William W. Cohen, Alexander Borgida, Haym Hirsh: Computing Least Common Subsumers in Description Logics. AAAI 1992: 754-760
9EEWilliam W. Cohen, Haym Hirsh: Learnability of Description Logics. COLT 1992: 116-127
1991
8 Haym Hirsh: Theoretical Underpinnings of Version Spaces. IJCAI 1991: 665-670
1990
7 Haym Hirsh: Learning from Data with Bounded Inconsistency. ML 1990: 32-39
6 Haym Hirsh: Incremental Version-Space Merging. ML 1990: 330-338
1989
5 Melissa P. Chase, Monte Zweben, Richard L. Piazza, John D. Burger, Paul P. Maglio, Haym Hirsh: Approximating Learned Search Control Knowledge. ML 1989: 218-220
4 Haym Hirsh: Combining Empirical and Analytical Learning with Version Spaces. ML 1989: 29-33
3 Scott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan: Incremental Batch Learning. ML 1989: 366-370
1988
2 Haym Hirsh: Reasoning about Operationality for Explanation-Based Learning. ML 1988: 214-220
1987
1 Haym Hirsh: Explanation-based Generalization in a Logic- Programming Environment. IJCAI 1987: 221-227

Coauthor Index

1Yonatan Aumann [34]
2Arunava Banerjee [36] [51] [56]
3Chumki Basu [20] [40] [44] [48]
4Alexander Borgida [10] [62]
5Bruce G. Buchanan [3]
6John D. Burger [5]
7Melissa P. Chase [5]
8Tze-Pin Chen [3]
9Steve A. Chien [53] [54]
10Scott H. Clearwater [3]
11William W. Cohen [9] [10] [14] [17] [19] [37] [40] [48] [64]
12Ido Dagan [31]
13Brian D. Davison [26] [27] [28] [36] [44]
14Aynur A. Dayanik [51] [56]
15Vasant Dhar [50]
16Thomas Ellman [33]
17Robert S. Engelmore [49]
18Ronen Feldman [21] [23] [31] [34]
19Moshe Fresko [34]
20Andrew Gelsey [24]
21Marti A. Hearst [43]
22Nathalie Japkowicz [18]
23Paul B. Kantor [20]
24Daniel Kudenko [22] [30] [39] [42]
25Orly Liphstat [34]
26Michael L. Littman [63]
27David Loewenstern [20]
28Sofus A. Macskassy [36] [50] [51] [56] [58]
29Paul P. Maglio [5]
30Chris Mesterharm [63]
31Nina Mishra [29] [59]
32Craig G. Nevill-Manning [48]
33Kwong Bor Ng [20]
34Michiel O. Noordewier [16]
35Steven W. Norton [11] [13]
36Richard L. Piazza [5]
37Leonard Pitt [29] [59]
38Foster J. Provost [50]
39Martin Rajman [34]
40Khaled Rasheed [24] [25] [41] [46]
41Ramesh Sankaranarayanan [50]
42Jonathan Schler (Yonatan Schler) [34]
43Mark Schwabacher [33]
44Matthew Stone [60]
45Alexander L. Strehl [63]
46Thomas Walsh [62]
47Gary M. Weiss [35] [38] [47]
48Sarah Zelikovitz [45] [52] [55] [57] [61] [64]
49Monte Zweben [5]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)