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Haym Hirsh
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- affiliation: Rutgers University, USA
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2020 – today
- 2020
- [j26]Jaron Porciello
, Maryia Ivanina
Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning. Nat. Mach. Intell. 2(10): 559-565 (2020)
2010 – 2019
- 2016
- [c50]Paul Upchurch, Daniel Sedra, Andrew Mullen, Haym Hirsh, Kavita Bala
Interactive Consensus Agreement Games for Labeling Images. HCOMP 2016: 239-248 - 2015
- [i3]Pietro Michelucci, Lea Shanley, Janis Dickinson, Haym Hirsh:
A U.S. Research Roadmap for Human Computation. CoRR abs/1505.07096 (2015) - 2014
- [j25]Seyda Ertekin, Cynthia Rudin, Haym Hirsh:
Approximating the crowd. Data Min. Knowl. Discov. 28(5-6): 1189-1221 (2014) - [j24]David W. McDonald, David H. Ackley, Randal E. Bryant, Melissa Gedney, Haym Hirsh, Lea Shanley
Antisocial computing: exploring design risks in social computing systems. Interactions 21(6): 72-75 (2014) - 2013
- [j23]Rezarta Islamaj Dogan
Reports on the 2012 AAAI Fall Symposium Series. AI Mag. 34(1): 93-100 (2013) - [r1]Haym Hirsh:
Human Computation in the Wild. Handbook of Human Computation 2013: 89-93 - 2012
- [c49]Will Bridewell, Yolanda Gil, Haym Hirsh, Kerstin Kleese van Dam, Karsten Steinhaeuser:
Preface. AAAI Fall Symposium: Discovery Informatics 2012 - [c48]Seyda Ertekin, Haym Hirsh, Cynthia Rudin:
Selective Sampling of Labelers for Approximating the Crowd. AAAI Fall Symposium: Machine Aggregation of Human Judgment 2012 - [c47]Yolanda Gil, Haym Hirsh:
Discovery Informatics: AI Opportunities in Scientific Discovery. AAAI Fall Symposium: Discovery Informatics 2012 - [c46]Yotam I. Gingold, Etienne Vouga, Eitan Grinspun, Haym Hirsh:
Diamonds From the Rough: Improving Drawing, Painting, and Singing via Crowdsourcing. HCOMP@AAAI 2012 - [i2]Seyda Ertekin, Haym Hirsh, Cynthia Rudin:
Learning to Predict the Wisdom of Crowds. CoRR abs/1204.3611 (2012) - 2011
- [i1]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)
2000 – 2009
- 2009
- [j22]Razvan C. Bunescu, Vitor R. Carvalho, Jan Chomicki, Vincent Conitzer, Michael T. Cox, Virginia Dignum, Zachary Dodds, Mark Dredze
AAAI 2008 Workshop Reports. AI Mag. 30(1): 108-118 (2009) - 2008
- [j21]Haym Hirsh:
Data Mining Research: Current Status and Future Opportunities. Stat. Anal. Data Min. 1(2): 104-107 (2008) - 2007
- [j20]Sarah Zelikovitz, William W. Cohen, Haym Hirsh:
Extending WHIRL with background knowledge for improved text classification. Inf. Retr. 10(1): 35-67 (2007) - 2006
- [c45]Alexander L. Strehl, Chris Mesterharm, Michael L. Littman, Haym Hirsh:
Experience-efficient learning in associative bandit problems. ICML 2006: 889-896 - 2005
- [j19]Matthew Stone, Haym Hirsh:
Artificial Intelligence: The Next Twenty-Five Years. AI Mag. 26(4): 85-97 (2005) - [c44]Alexander Borgida, Thomas J. Walsh, Haym Hirsh:
Towards Measuring Similarity in Description Logics. Description Logics 2005 - [c43]Sarah Zelikovitz, Haym Hirsh:
Improving Text Classification Using EM with Background Text. FLAIRS 2005: 499-505 - 2004
- [j18]Haym Hirsh, Nina Mishra, Leonard Pitt:
Version spaces and the consistency problem. Artif. Intell. 156(2): 115-138 (2004) - 2003
- [j17]Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee
Converting numerical classification into text classification. Artif. Intell. 143(1): 51-77 (2003) - [c42]Sofus A. Macskassy, Haym Hirsh:
Adding numbers to text classification. CIKM 2003: 240-246 - [c41]Sarah Zelikovitz, Haym Hirsh:
Integrating Background Knowledge Into Text Classification. IJCAI 2003: 1448-1449 - 2002
- [j16]Steve A. Chien, Haym Hirsh:
Editorial Introduction: The Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2001). AI Mag. 23(2): 9-10 (2002) - [c40]Sarah Zelikovitz, Haym Hirsh:
Integrating Background Knowledge into Nearest-Neighbor Text Classification. ECCBR 2002: 1-5 - 2001
- [j15]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 Mag. 22(2): 13-14 (2001) - [j14]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. 14: 231-252 (2001) - [c39]Sarah Zelikovitz, Haym Hirsh:
Using LSI for Text Classification in the Presence of Background Text. CIKM 2001: 113-118 - [c38]Sofus A. Macskassy, Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik:
Using Text Classifiers for Numerical Classification. IJCAI 2001: 885-890 - [c37]Sofus A. Macskassy, Haym Hirsh, Foster J. Provost, Ramesh Sankaranarayanan, Vasant Dhar:
Intelligent Information Triage. SIGIR 2001: 318-326 - [e2]Haym Hirsh, Steve A. Chien:
Proceedings of the Thirteenth Innovative Applications of Artificial Intelligence Conference, August 7-9, 2001, Seattle, Washington, USA. AAAI 2001, ISBN 1-57735-134-7 [contents] - 2000
- [j13]Haym Hirsh, Chumki Basu, Brian D. Davison
Enabling technologies: learning to personalize. Commun. ACM 43(8): 102-106 (2000) - [j12]Marti A. Hearst, Haym Hirsh:
AI's Greatest Trends and Controversies. IEEE Intell. Syst. 15(1): 8-17 (2000) - [c36]Gary M. Weiss, Haym Hirsh:
A Quantitative Study of Small Disjuncts. AAAI/IAAI 2000: 665-670 - [c35]Khaled Rasheed, Haym Hirsh:
Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models. GECCO 2000: 628-635 - [c34]Sarah Zelikovitz, Haym Hirsh:
Improving Short-Text Classification using Unlabeled Data for Classification Problems. ICML 2000: 1191-1198
1990 – 1999
- 1999
- [j11]Khaled Rasheed, Haym Hirsh:
Learning to be selective in genetic-algorithm-based design optimization. Artif. Intell. Eng. Des. Anal. Manuf. 13(3): 157-169 (1999) - [j10]Haym Hirsh, Michael H. Coen, Michael C. Mozer, Richard Hasha, James L. Flanagan:
Room service, AI-style. IEEE Intell. Syst. 14(2): 8-19 (1999) - [c33]Daniel Kudenko, Haym Hirsh:
Feature-Based Learners for Description Logics. Description Logics 1999 - 1998
- [j9]Mark Schwabacher, Thomas Ellman, Haym Hirsh:
Learning to set up numerical optimizations of engineering designs. Artif. Intell. Eng. Des. Anal. Manuf. 12(2): 173-192 (1998) - [j8]Haym Hirsh:
Trends & Controversies: Interactive Fiction. IEEE Intell. Syst. 13(6): 12-21 (1998) - [j7]Ronen Feldman, Ido Dagan, Haym Hirsh:
Mining Text Using Keyword Distributions. J. Intell. Inf. Syst. 10(3): 281-300 (1998) - [c32]Chumki Basu, Haym Hirsh, William W. Cohen:
Recommendation as Classification: Using Social and Content-Based Information in Recommendation. AAAI/IAAI 1998: 714-720 - [c31]Daniel Kudenko, Haym Hirsh:
Feature Generation for Sequence Categorization. AAAI/IAAI 1998: 733-738 - [c30]Gary M. Weiss, Haym Hirsh:
The Problem with Noise and Small Disjuncts. ICML 1998: 574- - [c29]William W. Cohen, Haym Hirsh:
Joins that Generalize: Text Classification Using WHIRL. KDD 1998: 169-173 - [c28]Sofus A. Macskassy, Arunava Banerjee, Brian D. Davison, Haym Hirsh:
Human Performance on Clustering Web Pages: A Preliminary Study. KDD 1998: 264-268 - [c27]Gary M. Weiss, Haym Hirsh:
Learning to Predict Rare Events in Event Sequences. KDD 1998: 359-363 - [c26]Ronen Feldman, Moshe Fresko, Haym Hirsh, Yonatan Aumann, Orly Liphstat, Yonatan Schler, Martin Rajman:
Knowledge Management: A Text Mining Approach. PAKM 1998 - 1997
- [j6]Khaled Rasheed, Haym Hirsh, Andrew Gelsey:
A genetic algorithm for continuous design space search. Artif. Intell. Eng. 11(3): 295-305 (1997) - [j5]Ronen Feldman, Haym Hirsh:
Exploiting Background Information in Knowledge Discovery from Text. J. Intell. Inf. Syst. 9(1): 83-97 (1997) - [c25]Haym Hirsh, Daniel Kudenko:
Representing Sequences in Description Logics. AAAI/IAAI 1997: 384-389 - [c24]Haym Hirsh, Nina Mishra, Leonard Pitt:
Version Spaces without Boundary Sets. AAAI/IAAI 1997: 491-496 - [c23]Brian D. Davison, Haym Hirsh:
Experiments in UNIX Command Prediction. AAAI/IAAI 1997: 827 - [c22]Haym Hirsh, Brian D. Davison
An Adaptive UNIX Command-Line Assistant. Agents 1997: 542-543 - [c21]Brian D. Davison, Haym Hirsh:
Toward an Adaptive Command Line Interface. HCI (2) 1997: 505-508 - [c20]Khaled Rasheed, Haym Hirsh:
Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization. ICGA 1997: 513-520 - 1996
- [j4]Mark Schwabacher, Thomas Ellman, Haym Hirsh:
Inductive learning for engineering design optimization. Artif. Intell. Eng. Des. Anal. Manuf. 10(2): 179-180 (1996) - [c19]Daniel Kudenko, Haym Hirsh:
Representing Sequences in Description Logics Using Suffix Trees. Description Logics 1996: 141-145 - [c18]Ronen Feldman, Haym Hirsh:
Mining Associations in Text in the Presence of Background Knowledge. KDD 1996: 343-346 - [c17]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
- [c16]William W. Cohen, Haym Hirsh:
Corrigendum for "Learnability of Description Logics". COLT 1995: 463 - 1994
- [j3]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) - [j2]Haym Hirsh:
Generalizing Version Spaces. Mach. Learn. 17(1): 5-46 (1994) - [j1]William W. Cohen, Haym Hirsh:
The Learnability of Description Logics with Equality Constraints. Mach. Learn. 17(2-3): 169-199 (1994) - [c15]Haym Hirsh, Nathalie Japkowicz:
Bootstrapping Training-Data Representations for Inductive Learning: A Case Study in Molecular Biology. AAAI 1994: 639-644 - [c14]William W. Cohen, Haym Hirsh:
Learning the Classic Description Logic: Theoretical and Experimental Results. KR 1994: 121-133 - [e1]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, ISBN 1-55860-335-2 [contents] - [d1]Haym Hirsh:
Badges. UCI Machine Learning Repository, 1994 - 1993
- [c13]Steven W. Norton, Haym Hirsh:
Learning DNF Via Probabilistic Evidence Combination. ICML 1993: 220-227 - 1992
- [c12]Haym Hirsh:
Polynomial-Time Learning with Version Spaces. AAAI 1992: 117-122 - [c11]Steven W. Norton, Haym Hirsh:
Classifier Learning from Noisy Data as Probabilistic Evidence Combination. AAAI 1992: 141-146 - [c10]William W. Cohen, Alexander Borgida, Haym Hirsh:
Computing Least Common Subsumers in Description Logics. AAAI 1992: 754-760 - [c9]William W. Cohen, Haym Hirsh:
Learnability of Description Logics. COLT 1992: 116-127 - 1991
- [c8]Haym Hirsh:
Theoretical Underpinnings of Version Spaces. IJCAI 1991: 665-670 - 1990
- [c7]Haym Hirsh:
Learning from Data with Bounded Inconsistency. ML 1990: 32-39 - [c6]Haym Hirsh:
Incremental Version-Space Merging. ML 1990: 330-338
1980 – 1989
- 1989
- [b1]Haym Hirsh:
Incremental version-space merging : a general framework for concept learning. Stanford University, USA, 1989 - [c5]Haym Hirsh:
Combining Empirical and Analytical Learning with Version Spaces. ML 1989: 29-33 - [c4]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 - [c3]Scott H. Clearwater, Tze-Pin Chen, Haym Hirsh, Bruce G. Buchanan:
Incremental Batch Learning. ML 1989: 366-370 - 1988
- [c2]Haym Hirsh:
Reasoning about Operationality for Explanation-Based Learning. ML 1988: 214-220 - 1987
- [c1]Haym Hirsh:
Explanation-based Generalization in a Logic-Programming Environment. IJCAI 1987: 221-227
Coauthor Index
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