Haym Hirsh

	2012
70		Seyda Ertekin, Haym Hirsh, Cynthia Rudin: Learning to Predict the Wisdom of Crowds CoRR abs/1204.3611: (2012)
	2011
69		Chumki Basu, William W. Cohen, Haym Hirsh, Craig G. Nevill-Manning: Technical Paper Recommendation: A Study in Combining Multiple Information Sources CoRR abs/1106.0248: (2011)
	2009
68		Razvan C. Bunescu, Vitor R. Carvalho, Jan Chomicki, Vincent Conitzer, Michael T. Cox, Virginia Dignum, Zachary Dodds, Mark Dredze, David Furcy, Evgeniy Gabrilovich, Mehmet H. Göker, Hans W. Guesgen, Haym Hirsh, Dietmar Jannach, Ulrich Junker, Wolfgang Ketter, Alfred Kobsa, Sven Koenig, Tessa A. Lau, Lundy Lewis, Eric T. Matson, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Joelle Pineau, Pascal Poupart, Anita Raja, Wheeler Ruml, Norman M. Sadeh, Guy Shani, Daniel G. Shapiro, Sarabjot Singh Anand, Matthew E. Taylor, Kiri Wagstaff, Trey Smith, William E. Walsh, Ron Zhou: AAAI 2008 Workshop Reports. AI Magazine 30(1): 108-118 (2009)
	2008
67		Haym Hirsh: Data Mining Research: Current Status and Future Opportunities. Statistical Analysis and Data Mining 1(2): 104-107 (2008)
	2007
66		Sarah Zelikovitz, William W. Cohen, Haym Hirsh: Extending WHIRL with background knowledge for improved text classification. Inf. Retr. 10(1): 35-67 (2007)
	2006
65		Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh: Experience-efficient learning in associative bandit problems. ICML 2006: 889-896
	2005
64		Alexander Borgida, Thomas Walsh, Haym Hirsh: Towards Measuring Similarity in Description Logics. Description Logics 2005
63		Sarah Zelikovitz, Haym Hirsh: Improving Text Classification Using EM with Background Text. FLAIRS Conference 2005: 499-505
62		Matthew Stone, Haym Hirsh: Artificial Intelligence: The Next Twenty-Five Years. AI Magazine 26(4): 85-97 (2005)
	2004
61		Haym Hirsh, Nina Mishra, Leonard Pitt: Version spaces and the consistency problem. Artif. Intell. 156(2): 115-138 (2004)
	2003
60		Sofus A. Macskassy, Haym Hirsh: Adding numbers to text classification. CIKM 2003: 240-246
59		Sarah Zelikovitz, Haym Hirsh: Integrating Background Knowledge Into Text Classification. IJCAI 2003: 1448-1449
58		Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik: Converting numerical classification into text classification. Artif. Intell. 143(1): 51-77 (2003)
	2002
57		Sarah Zelikovitz, Haym Hirsh: Integrating Background Knowledge into Nearest-Neighbor Text Classification. ECCBR 2002: 1-5
56		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
55		Haym Hirsh, Steve A. Chien: Proceedings of the Thirteenth Innovative Applications of Artificial Intelligence Conference, August 7-9, 2001, Seattle, Washington, USA AAAI 2001
54		Sarah Zelikovitz, Haym Hirsh: Using LSI for Text Classification in the Presence of Background Text. CIKM 2001: 113-118
53		Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik: Using Text Classifiers for Numerical Classification. IJCAI 2001: 885-890
52		Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar: Intelligent Information Triage. SIGIR 2001: 318-326
51		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)
50		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
49		Gary M. Weiss, Haym Hirsh: A Quantitative Study of Small Disjuncts. AAAI/IAAI 2000: 665-670
48		Khaled Rasheed, Haym Hirsh: Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models. GECCO 2000: 628-635
47		Sarah Zelikovitz, Haym Hirsh: Improving Short-Text Classification using Unlabeled Data for Classification Problems. ICML 2000: 1191-1198
46		Haym Hirsh, Chumki Basu, Brian D. Davison: Enabling technologies: learning to personalize. Commun. ACM 43(8): 102-106 (2000)
45		Marti A. Hearst, Haym Hirsh: AI's Greatest Trends and Controversies. IEEE Intelligent Systems 15(1): 8-17 (2000)
	1999
44		Daniel Kudenko, Haym Hirsh: Feature-Based Learners for Description Logics. Description Logics 1999
43		Khaled Rasheed, Haym Hirsh: Learning to be selective in genetic-algorithm-based design optimization. AI EDAM 13(3): 157-169 (1999)
	1998
42		Chumki Basu, Haym Hirsh, William W. Cohen: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. AAAI/IAAI 1998: 714-720
41		Daniel Kudenko, Haym Hirsh: Feature Generation for Sequence Categorization. AAAI/IAAI 1998: 733-738
40		Gary M. Weiss, Haym Hirsh: The Problem with Noise and Small Disjuncts. ICML 1998: 574-
39		William W. Cohen, Haym Hirsh: Joins that Generalize: Text Classification Using WHIRL. KDD 1998: 169-173
38		Sofus A. Macskassy, Arunava Banerjee, Brian D. Davison, Haym Hirsh: Human Performance on Clustering Web Pages: A Preliminary Study. KDD 1998: 264-268
37		Gary M. Weiss, Haym Hirsh: Learning to Predict Rare Events in Event Sequences. KDD 1998: 359-363
36		Ronen Feldman, Moshe Fresko, Haym Hirsh, Yonatan Aumann, Orly Liphstat, Yonatan Schler, Martin Rajman: Knowledge Management: A Text Mining Approach. PAKM 1998
35		Mark Schwabacher, Thomas Ellman, Haym Hirsh: Learning to set up numerical optimizations of engineering designs. AI EDAM 12(2): 173-192 (1998)
34		Haym Hirsh: Trends & Controversies: Interactive Fiction. IEEE Intelligent Systems 13(6): 12-21 (1998)
33		Ronen Feldman, Ido Dagan, Haym Hirsh: Mining Text Using Keyword Distributions. J. Intell. Inf. Syst. 10(3): 281-300 (1998)
	1997
32		Haym Hirsh, Daniel Kudenko: Representing Sequences in Description Logics. AAAI/IAAI 1997: 384-389
31		Haym Hirsh, Nina Mishra, Leonard Pitt: Version Spaces without Boundary Sets. AAAI/IAAI 1997: 491-496
30		Brian D. Davison, Haym Hirsh: Experiments in UNIX Command Prediction. AAAI/IAAI 1997: 827
29		Haym Hirsh, Brian D. Davison: An Adaptive UNIX Command-Line Assistant. Agents 1997: 542-543
28		Brian D. Davison, Haym Hirsh: Toward an Adaptive Command Line Interface. HCI (2) 1997: 505-508
27		Khaled Rasheed, Haym Hirsh: Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization. ICGA 1997: 513-520
26		Khaled Rasheed, Haym Hirsh, Andrew Gelsey: A genetic algorithm for continuous design space search. AI in Engineering 11(3): 295-305 (1997)
25		Ronen Feldman, Haym Hirsh: Exploiting Background Information in Knowledge Discovery from Text. J. Intell. Inf. Syst. 9(1): 83-97 (1997)
	1996
24		Daniel Kudenko, Haym Hirsh: Representing Sequences in Description Logics Using Suffix Trees. Description Logics 1996: 141-145
23		Ronen Feldman, Haym Hirsh: Mining Associations in Text in the Presence of Background Knowledge. KDD 1996: 343-346
22		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
21		Mark Schwabacher, Thomas Ellman, Haym Hirsh: Inductive learning for engineering design optimization. AI EDAM 10(2): 179-180 (1996)
	1995
20		William W. Cohen, Haym Hirsh: Corrigendum for ``Learnability of Description Logics''. COLT 1995: 463
	1994
19		William W. Cohen, Haym Hirsh: Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994 Morgan Kaufmann 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		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		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

Coauthor Index

1	Sarabjot S. Anand (Sarabjot Singh Anand, Sarab S. Anand)	[68]
2	Yonatan Aumann	[36]
3	Arunava Banerjee	[38] [53] [58]
4	Chumki Basu	[22] [42] [46] [50] [69]
5	Alexander Borgida	[10] [64]
6	Bruce G. Buchanan	[3]
7	Razvan C. Bunescu	[68]
8	John D. Burger	[5]
9	Vitor R. Carvalho (Vitor Rocha de Carvalho)	[68]
10	Melissa P. Chase	[5]
11	Tze-Pin Chen	[3]
12	Steve A. Chien	[55] [56]
13	Jan Chomicki	[68]
14	Scott H. Clearwater	[3]
15	William W. Cohen	[9] [10] [14] [17] [19] [20] [39] [42] [50] [66] [69]
16	Vincent Conitzer	[68]
17	Michael T. Cox	[68]
18	Ido Dagan	[33]
19	Brian D. Davison	[28] [29] [30] [38] [46]
20	Aynur A. Dayanik	[53] [58]
21	Vasant Dhar	[52]
22	Virginia Dignum	[68]
23	Zachary Dodds	[68]
24	Mark Dredze	[68]
25	Thomas Ellman	[21] [35]
26	Robert S. Engelmore	[51]
27	Seyda Ertekin	[70]
28	Ronen Feldman	[23] [25] [33] [36]
29	Moshe Fresko	[36]
30	David Furcy	[68]
31	Evgeniy Gabrilovich	[68]
32	Andrew Gelsey	[26]
33	Mehmet H. Göker	[68]
34	Hans W. Guesgen (Hans Werner Guesgen)	[68]
35	Marti A. Hearst	[45]
36	Dietmar Jannach	[68]
37	Nathalie Japkowicz	[18]
38	Ulrich Junker	[68]
39	Paul B. Kantor	[22]
40	Wolfgang Ketter	[68]
41	Alfred Kobsa	[68]
42	Sven Koenig	[68]
43	Daniel Kudenko	[24] [32] [41] [44]
44	Tessa A. Lau (Tessa Lau)	[68]
45	Lundy Lewis	[68]
46	Orly Liphstat	[36]
47	Michael L. Littman	[65]
48	David Loewenstern	[22]
49	Sofus A. Macskassy	[38] [52] [53] [58] [60]
50	Paul P. Maglio	[5]
51	Eric T. Matson	[68]
52	Chris Mesterharm	[65]
53	Ted Metzler	[68]
54	Rada Mihalcea	[68]
55	Nina Mishra	[31] [61]
56	Bamshad Mobasher	[68]
57	Craig G. Nevill-Manning	[50] [69]
58	Kwong Bor Ng	[22]
59	Michiel O. Noordewier	[16]
60	Steven W. Norton	[11] [13]
61	Richard L. Piazza	[5]
62	Joelle Pineau	[68]
63	Leonard Pitt	[31] [61]
64	Pascal Poupart	[68]
65	Foster J. Provost	[52]
66	Anita Raja	[68]
67	Martin Rajman	[36]
68	Khaled Rasheed	[26] [27] [43] [48]
69	Cynthia Rudin	[70]
70	Wheeler Ruml	[68]
71	Norman M. Sadeh	[68]
72	Ramesh Sankaranarayanan	[52]
73	Jonathan Schler (Yonatan Schler)	[36]
74	Mark Schwabacher	[21] [35]
75	Guy Shani	[68]
76	Daniel G. Shapiro	[68]
77	Trey Smith	[68]
78	Matthew Stone	[62]
79	Alexander L. Strehl	[65]
80	Matthew E. Taylor (Matthew Edmund Taylor)	[68]
81	Kiri Wagstaff (Kiri L. Wagstaff)	[68]
82	Thomas Walsh	[64]
83	William E. Walsh	[68]
84	Gary M. Weiss	[37] [40] [49]
85	Sarah Zelikovitz	[47] [54] [57] [59] [63] [66]
86	Ron Zhou	[68]
87	Monte Zweben	[5]

Colors in the list of coauthors

Last update Thu May 31 18:55:10 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page