Haym Hirsh

发表

Haym Hirsh, M. H. Coen, M. C. Mozer, 1999, IEEE Intell. Syst..

Approximating Learned Search Control Knowledge

Paul P. Maglio, Haym Hirsh, John D. Burger, 1989, ML.

Learning to set up numerical optimizations of engineering designs

Haym Hirsh, Mark Schwabacher, Thomas Ellman, 1998, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

Discovery Informatics: AI Opportunities in Scientific Discovery

Yolanda Gil, Haym Hirsh, H. Hirsh, 2012, AAAI Fall Symposium: Discovery Informatics.

Antisocial computing

David H. Ackley, David W. McDonald, Haym Hirsh, 2014, Interactions.

Using background knowledge to improve inductive learning of DNA sequences

Haym Hirsh, M. Noordewier, 1994, Proceedings of the Tenth Conference on Artificial Intelligence for Applications.

Version Spaces without Boundary Sets

Leonard Pitt, Haym Hirsh, Nina Mishra, 1997, AAAI/IAAI.

Representing Sequences in Description Logics

Daniel Kudenko, Haym Hirsh, 1997, AAAI/IAAI.

Toward an Adaptive Command Line Interface

Brian D. Davison, Haym Hirsh, 1997, HCI.

Polynomial-Time Learning with Version Spaces

Haym Hirsh, 1992, AAAI.

Converting Numerical Classication into Text Classication

Haym Hirsh, Arunava Banerjee, Aynur A. Dayanik, 2003 .

Information Valets: Adaptivity for Multi-Platform Access to Heterogeneous Information

Haym Hirsh, Sofus A. Macskassy, Aynur A. Dayanik, 2000 .

Improving on-line learning

Haym Hirsh, Jon Christian Mesterharm, H. Hirsh, 2007 .

Keyword-Based Browsing and Analysis of Large Document Sets

Ido Dagan, Haym Hirsh, Ronen Feldman, 1996 .

Data Fusion of Machine-Learning Methods for the TREC5 Routing Task (and other work)

Paul B. Kantor, Kwong Bor Ng, Haym Hirsh, 1996, TREC.

Learning to Predict Rare Events in Event Sequences

Haym Hirsh, Gary M. Weiss, H. Hirsh, 1998, KDD.

Approximating the crowd

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2014, Data Mining and Knowledge Discovery.

Learning to set up numerical optimizations of engineering designs

Haym Hirsh, Mark Schwabacher, Thomas Ellman, 1998 .

Computing Least Common Subsumers in Description Logics

Alexander Borgida, William W. Cohen, Haym Hirsh, 1992, AAAI.

Learning the Classic Description Logic: Theoretical and Experimental Results

William W. Cohen, Haym Hirsh, H. Hirsh, 1994, KR.

Discovery informatics : the role of AI research in innovating scientific processes : papers from the AAAI Fall Symposium

Yolanda Gil, Haym Hirsh, 2012 .

Learning to Choose a Reformulation for Numerical Optimization of Engineering Designs

Haym Hirsh, Mark Schwabacher, Thomas Ellman, 1996 .

Extending Domain Theories: Two Case Studies in Student Modeling

Haym Hirsh, D. Sleeman, Ian Ellery, 1990, Machine Learning.

Reasoning about Operationality for Explanation-Based Learning

Haym Hirsh, H. Hirsh, 1988, ML.

Generalizing version spaces

Haym Hirsh, 1994, Machine Learning.

Extending WHIRL with background knowledge for improved text classification

William W. Cohen, Haym Hirsh, Sarah Zelikovitz, 2006, Information Retrieval.

Selective Sampling of Labelers for Approximating the Crowd

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2012, AAAI Fall Symposium: Machine Aggregation of Human Judgment.

Human Performance on Clustering Web Pages: A Preliminary Study

Brian D. Davison, Haym Hirsh, Arunava Banerjee, 1998, KDD.

Artificial Intelligence: The Next Twenty-Five Years

Matthew Stone, Haym Hirsh, 2005, AI Mag..

Editorial Introduction to this Special Issue of AI Magazine: The Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000)

Haym Hirsh, Robert S. Engelmore, 2001, AI Mag..

AAAI 2008 Workshop Reports

Vincent Conitzer, Guy Shani, Alfred Kobsa, 2009, AI Mag..

Learning to Predict the Wisdom of Crowds pdf

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2012, ArXiv.

Using background knowledge to improve text classification

Haym Hirsh, Sarah Zelikovitz, H. Hirsh, 2002 .

Using background knowledge to improve inductive learning: a case study in molecular biology

Haym Hirsh, Michiel O. Noordewier, 1994, IEEE Expert.

Representing Sequences in Description Logics Using Suffix Trees

Daniel Kudenko, Haym Hirsh, 1996, Description Logics.

Using LSI for text classification in the presence of background text

Haym Hirsh, Sarah Zelikovitz, H. Hirsh, 2001, CIKM '01.

Inductive learning for engineering design optimization

Haym Hirsh, Mark Schwabacher, Thomas Ellman, 1996, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

Exploiting Background Information in Knowledge Discovery from Text

Haym Hirsh, Ronen Feldman, H. Hirsh, 1997, Journal of Intelligent Information Systems.

Binning: Converting Numerical Classification into Text Classification

Haym Hirsh, Sofus A. Macskassy, Aynur A. Dayanik, 2000 .

Enabling technologies: learning to personalize.

Haym Hirsh, Chumki Basu, Brian D. Davison, 2000 .

Towards Measuring Similarity in Description Logics

Alexander Borgida, Thomas J. Walsh, Haym Hirsh, 2005, Description Logics.

Intelligent information triage

Vasant Dhar, Haym Hirsh, Foster J. Provost, 2001, SIGIR '01.

The effect of small disjuncts and class distribution on decision tree learning

Haym Hirsh, Gary M. Weiss, H. Hirsh, 2003 .

Experience-efficient learning in associative bandit problems

Chris Mesterharm, Haym Hirsh, Michael L. Littman, 2006, ICML.

Gado: a genetic algorithm for continuous design optimization

Haym Hirsh, Khaled Rasheed, H. Hirsh, 1998 .

Learning to Predict Extremely Rare Events

Haym Hirsh, Gary M. Weiss, H. Hirsh, 2000 .

Learning to Predict Rare Events in Categorical Time-Series Data

Haym Hirsh, Gary M. Weiss, 1998 .

Learning DNF Via Probabilistic Evidence Combination

Haym Hirsh, Steven W. Norton, S. W. Norton, 1993, ICML.

Corrigendum for "learnability of description logics"

William W. Cohen, Haym Hirsh, 1995, COLT '95.

Jambalaya: Using Multicast for Blind Distributed Web Searching and Advertising

Haym Hirsh, Julio C. Navasand, H. Hirsh, 1998 .

Imposing bounds on the number of categories for incremental concept formation

Haym Hirsh, Leon Shklar, 1997, COLT 1997.

Wisely Using a Budget for Crowdsourcing

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2012 .

Jambalaya: Using Multicast for Blind Distributed Web

Haym Hirsh, Julio C. Navas, 1998 .

Knowledge Management: A Text Mining Approach

Yonatan Aumann, Haym Hirsh, Martin Rajman, 1998, PAKM.

Improving Short-Text Classification using Unlabeled Data for Classification Problems

Haym Hirsh, Sarah Zelikovitz, H. Hirsh, 2000, ICML.

Using Text Classifiers for Numerical Classification

Haym Hirsh, Arunava Banerjee, Sofus A. Macskassy, 2001, IJCAI.

Interactive Consensus Agreement Games for Labeling Images

Haym Hirsh, Kavita Bala, Paul Upchurch, 2016, HCOMP.

EmailValet: Learning User Preferences for Wireless Email

Haym Hirsh, Sofus A. Macskassy, Aynur A. Dayanik, 1999 .

Data Mining Research: Current Status and Future Opportunities

Haym Hirsh, 2008 .

EmailValet: Learning Email Preferences for Wireless Platforms

Haym Hirsh, Sofus A. Macskassy, Aynur A. Dayanik, 1999 .

A Quantitative Study of Small Disjuncts: Experiments and Results *

Haym Hirsh, Gary M. Weiss, H. Hirsh, 2000 .

Mining Text Using Keyword Distributions

Ido Dagan, Haym Hirsh, Ronen Feldman, 1998, Journal of Intelligent Information Systems.

Overgenerality in Explanation-Based Generalization

Haym Hirsh, H. Hirsh, 1990 .

Recommendation as Classification: Using Social and Content-Based Information in Recommendation

William W. Cohen, Haym Hirsh, Chumki Basu, 1998, AAAI/IAAI.

EmailValet: Where do you want to read your Email?

Haym Hirsh, Sofus A. Macskassy, Aynur A. Dayanik, 2001 .

Recommendation as classification and recommendation as matching: two information-centered approaches to recommendation

Haym Hirsh, Chumki Basu, 2002 .

Incremental batch learning

Haym Hirsh, Bruce G. Buchanan, Scott H. Clearwater, 1989, ICML 1989.

Incremental Version-Space Merging

Haym Hirsh, 1990, ML.

AI's Greatest Trends and Controversies

Haym Hirsh, Marti A. Hearst, 2000, IEEE Intell. Syst..

Information Valets for Intelligent Information Access

Haym Hirsh, Sofus A. Macskassy, Aynur A. Dayanik, 2000 .

Learnability of description logics

William W. Cohen, Haym Hirsh, H. Hirsh, 1992, COLT '92.

A Quantum Leap for AI

Haym Hirsh, 1999 .

Inference of incorrect operators

Haym Hirsh, Derek Sleeman, 1986 .

Probabilistic Online Action Prediction

Brian D. Davison, Haym Hirsh, H. Hirsh, 1998 .

Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference

Steve Chien, Haym Hirsh, 2001 .

Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models

Haym Hirsh, Khaled Rasheed, H. Hirsh, 2000, GECCO.

Reports on the 2012 AAAI Fall Symposium Series

Samarth Swarup, Parisa Rashidi, Wei Sun, 2013, AI Mag..

The learnability of description logics with equality constraints

William W. Cohen, Haym Hirsh, H. Hirsh, 1994, Machine Learning.

Combining Empirical and Analytical Learning

Haym Hirsh, 1990 .

Human Computation in the Wild

Haym Hirsh, 2013, Handbook of Human Computation.

Inductive learning of feature-tracking rules for scientific visualization

Haym Hirsh, Thomas Ellman, Arunava Banerjee, 1995 .

Human Performance on Clustering Web Pages

Brian D. Davison, Haym Hirsh, Arunava Banerjee, 1998 .

Learning prototype-selection rules for case-based iterative design

Haym Hirsh, Mark Schwabacher, Thomas Ellman, 1994, Proceedings of the Tenth Conference on Artificial Intelligence for Applications.

Learning to be selective in genetic-algorithm-based design optimization

Haym Hirsh, Khaled Rasheed, H. Hirsh, 1999, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

Machine Learning, Proceedings of the Eleventh International Conference, Rutgers University, New Brunswick, NJ, USA, July 10-13, 1994

William W. Cohen, Haym Hirsh, H. Hirsh, 1994, ICML.

A Quantitative Study of Small Disjuncts

Haym Hirsh, Gary M. Weiss, H. Hirsh, 2000, AAAI/IAAI.

TWO FRAMEWORKS FOR INTEGRATING KNOWLEDGE IN INDUCTION

Benjamin D. Smith, William W. Cohen, Haym Hirsh, 1993 .

The Problem with Noise and Small Disjuncts

Haym Hirsh, Gary M. Weiss, 1998, ICML.

The Candidate-Elimination Algorithm: Emulation and Extensions

Haym Hirsh, 1990 .

Knowledge as Bias

Haym Hirsh, 1990 .

Appears in User Modeling 2001 Workshop: Machine Learning, Information Retrieval and User Modeling Information Triage using Prospective Criteria

Vasant Dhar, Haym Hirsh, Foster Provost, 2001 .

The design and evaluation of web prefetching and caching techniques

Brian D. Davison, Haym Hirsh, H. Hirsh, 2002 .

Diamonds From the Rough: Improving Drawing, Painting, and Singing via Crowdsourcing

Eitan Grinspun, Haym Hirsh, Yotam I. Gingold, 2012, HCOMP@AAAI.

Improving Text Classification Using EM with Background Text

Haym Hirsh, Sarah Zelikovitz, 2005, FLAIRS Conference.

New techniques in intelligent information filtering

Haym Hirsh, Sofus A. Macskassy, Sofus A. Macskassy, 2003 .

Editorial Introduction: The Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2001)

Haym Hirsh, Steve A. Chien, 2002, AI Mag..

Experiments in UNIX Co

Brian D. Davison, Haym Hirsh, 1997 .

THE PHASE-SPACE DYNAMICS OF SYSTEMS OF SPIKING NEURONS

Haym Hirsh, Arunava Banerjee, H. Hirsh, 2001 .

Explanation-based Generalization in a Logic-Programming Environment

Haym Hirsh, H. Hirsh, 1987, IJCAI.

Version spaces and the consistency problem

Leonard Pitt, Haym Hirsh, Nina Mishra, 2004, Artif. Intell..

Bootstrapping Training-Data Representations for Inductive Learning: A Case Study in Molecular Biology

Haym Hirsh, Nathalie Japkowicz, 1994, AAAI.

Integrating Background Knowledge into Nearest-Neighbor Text Classification

Haym Hirsh, Sarah Zelikovitz, 2002, ECCBR.

Learning When Reformulation is Appropriate for Iterative Design

Haym Hirsh, Mark Schwabacher, Thomas Ellman, 2007 .

Classifier Learning from Noisy Data as Probabilistic Evidence Combination

Haym Hirsh, Steven W. Norton, S. W. Norton, 1992, AAAI.

Experiments in UNIX Command Prediction

Brian D. Davison, Haym Hirsh, H. Hirsh, 1997, AAAI/IAAI.

Feature Generation for Sequence Categorization

Daniel Kudenko, Haym Hirsh, H. Hirsh, 1998, AAAI/IAAI.

Proceedings of the Thirteenth Innovative Applications of Artificial Intelligence Conference, August 7-9, 2001, Seattle, Washington, USA

Steve Chien, Haym Hirsh, 2001, IAAI.

Learning from data with bounded inconsistency: theoretical and experimental results

William W. Cohen, Haym Hirsh, 1994, COLT 1994.

Joins that Generalize: Text Classification Using WHIRL

William W. Cohen, Haym Hirsh, 1998, KDD.

Predicting Sequences of User Actions

Brian D. Davison, Haym Hirsh, H. Hirsh, 1998 .

Learning from Data with Bounded Inconsistency

Haym Hirsh, 1990, ML.

Combining Empirical and Analytical Learning with Version Spaces

Haym Hirsh, 1989, ML.

A genetic algorithm for continuous design space search

Haym Hirsh, Andrew Gelsey, Khaled Rasheed, 1997, Artif. Intell. Eng..

Converting numerical classification into text classification

Haym Hirsh, Arunava Banerjee, Sofus A. Macskassy, 2003, Artif. Intell..

Adding numbers to text classification

Haym Hirsh, Sofus A. Macskassy, Sofus A. Macskassy, 2003, CIKM '03.

Integrating Background Knowledge Into Text Classification

Haym Hirsh, Sarah Zelikovitz, 2003, IJCAI.

Mining Associations in Text in the Presence of Background Knowledge

Haym Hirsh, Ronen Feldman, H. Hirsh, 1996, KDD.

Data Mining Research: Current Status and Future Opportunities

Haym Hirsh, 2008, Stat. Anal. Data Min..

An adaptive UNIX command-line assistant

Brian D. Davison, Haym Hirsh, H. Hirsh, 1997, AGENTS '97.

Feature-Based Learners for Description Logics

Daniel Kudenko, Haym Hirsh, H. Hirsh, 1999, Description Logics.

Improving Short Text Classification Using Unlabeled Background Knowledge

Haym Hirsh, Sarah Zelikovitz, H. Hirsh, 2000, ICML 2000.

Using Case Based Learning to Improve Genetic Algorithm Based Design Optimization

Haym Hirsh, Khaled Rasheed, H. Hirsh, 1997, ICGA.

Trends & Controversies: Interactive Fiction

Haym Hirsh, 1998, IEEE Intell. Syst..

Learning to personalize

Brian D. Davison, Haym Hirsh, Chumki Basu, 2000, CACM.

3.2 Stage 2: Inter-feature Extrapolation towards a Bootstrapping Approach to Constructive Induction

Haym Hirsh, Nathalie Japkowicz, Morgan Kaufmann, 1994 .

A U.S. Research Roadmap for Human Computation pdf

Haym Hirsh, Lea Shanley, Pietro Michelucci, 2015, ArXiv.

Generalizing Version Spaces

Haym Hirsh, H. Hirsh, 1994, Machine Learning.

Conditional operationality and explanation-based generalization

Haym Hirsh, H. Hirsh, 1990 .