2007 | ||
---|---|---|
64 | EE | Sarah Zelikovitz, William W. Cohen, Haym Hirsh: Extending WHIRL with background knowledge for improved text classification. Inf. Retr. 10(1): 35-67 (2007) |
2006 | ||
63 | EE | Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh: Experience-efficient learning in associative bandit problems. ICML 2006: 889-896 |
2005 | ||
62 | EE | Alexander 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 | ||
59 | EE | Haym Hirsh, Nina Mishra, Leonard Pitt: Version spaces and the consistency problem. Artif. Intell. 156(2): 115-138 (2004) |
2003 | ||
58 | EE | Sofus 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 | |
56 | EE | Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik: Converting numerical classification into text classification. Artif. Intell. 143(1): 51-77 (2003) |
2002 | ||
55 | EE | Sarah 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) | |
48 | EE | Chumki 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 | |
44 | EE | Haym Hirsh, Chumki Basu, Brian D. Davison: Enabling technologies: learning to personalize. Commun. ACM 43(8): 102-106 (2000) |
43 | EE | Marti A. Hearst, Haym Hirsh: AI's Greatest Trends and Controversies. IEEE Intelligent Systems 15(1): 8-17 (2000) |
1999 | ||
42 | EE | Daniel Kudenko, Haym Hirsh: Feature-Based Learners for Description Logics. Description Logics 1999 |
41 | EE | Khaled 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 | |
34 | EE | Ronen Feldman, Moshe Fresko, Haym Hirsh, Yonatan Aumann, Orly Liphstat, Yonatan Schler, Martin Rajman: Knowledge Management: A Text Mining Approach. PAKM 1998 |
33 | EE | Mark 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 | |
24 | EE | Khaled 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 | |
20 | EE | Kwong 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 | |
16 | EE | Haym 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 | |
9 | EE | William 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 |