Travelled to:1 × Germany
13 × USA
Collaborated with:∅ S.Zelikovitz G.M.Weiss W.W.Cohen S.A.Macskassy B.D.Davison R.Feldman S.W.Norton A.L.Strehl C.Mesterharm M.L.Littman A.Banerjee S.H.Clearwater T.Chen B.G.Buchanan F.J.Provost R.Sankaranarayanan V.Dhar M.P.Chase M.Zweben R.L.Piazza J.D.Burger P.P.Maglio
Talks about:learn (9) text (6) classif (5) problem (3) use (3) background (2) increment (2) knowledg (2) version (2) presenc (2)
Person: Haym Hirsh
DBLP: Hirsh:Haym
Facilitated 1 volumes:
EdContributed to:
2006
20032001
200120001998
1998
19971996
19941993199019891988Wrote 19 papers:
- ICML-2006-StrehlMLH #learning #problem
- Experience-efficient learning in associative bandit problems (ALS, CM, MLL, HH), pp. 889–896.
- CIKM-2003-MacskassyH #classification
- Adding numbers to text classification (SAM, HH), pp. 240–246.
- CIKM-2001-ZelikovitzH #classification #using
- Using LSI for Text Classification in the Presence of Background Text (SZ, HH), pp. 113–118.
- SIGIR-2001-MacskassyHPSD
- Intelligent Information Triage (SAM, HH, FJP, RS, VD), pp. 318–326.
- ICML-2000-ZelikovitzH #classification #problem #using
- Improving Short-Text Classification using Unlabeled Data for Classification Problems (SZ, HH), pp. 1191–1198.
- ICML-1998-WeissH #problem
- The Problem with Noise and Small Disjuncts (GMW, HH), p. 574–?.
- KDD-1998-CohenH #classification #using
- Joins that Generalize: Text Classification Using WHIRL (WWC, HH), pp. 169–173.
- KDD-1998-MacskassyBDH #case study #clustering #performance #web
- Human Performance on Clustering Web Pages: A Preliminary Study (SAM, AB, BDD, HH), pp. 264–268.
- KDD-1998-WeissH #learning #predict #sequence
- Learning to Predict Rare Events in Event Sequences (GMW, HH), pp. 359–363.
- HCI-SEC-1997-DavisonH #adaptation #interface #towards
- Toward an Adaptive Command Line Interface (BDD, HH), pp. 505–508.
- KDD-1996-FeldmanH #mining
- Mining Associations in Text in the Presence of Background Knowledge (RF, HH), pp. 343–346.
- KR-1994-CohenH #learning #logic
- Learning the Classic Description Logic: Theoretical and Experimental Results (WWC, HH), pp. 121–133.
- ICML-1993-NortonH #learning #probability
- Learning DNF Via Probabilistic Evidence Combination (SWN, HH), pp. 220–227.
- ML-1990-Hirsh #bound #consistency #learning #nondeterminism
- Learning from Data with Bounded Inconsistency (HH), pp. 32–39.
- ML-1990-Hish #incremental
- Incremental Version-Space Merging (HH), pp. 330–338.
- ML-1989-ChaseZPBMH #approximate
- Approximating Learned Search Control Knowledge (MPC, MZ, RLP, JDB, PPM, HH), pp. 218–220.
- ML-1989-ClearwaterCHB #incremental #learning
- Incremental Batch Learning (SHC, TPC, HH, BGB), pp. 366–370.
- ML-1989-Hirsh #empirical #learning
- Combining Empirical and Analytical Learning with Version Spaces (HH), pp. 29–33.
- ML-1988-Hirsh #learning #reasoning
- Reasoning about Operationality for Explanation-Based Learning (HH), pp. 214–220.