Travelled to:1 × Korea
9 × USA
Collaborated with:M.Craven G.G.Towell R.Maclin M.Shavlik T.Eliassi-Rad K.J.Cherkauer D.W.Opitz ∅ A.Doan F.Niu C.Ré B.Chen R.Ramakrishnan P.Tamma D.Page J.Bockhorst J.D.Glasner D.H.Fisher K.B.McKusick R.J.Mooney C.Gokhale S.Das J.F.Naughton N.Rampalli X.Zhu
Talks about:network (6) neural (5) learn (5) use (5) knowledg (3) refin (3) base (3) artifici (2) approach (2) extract (2)
Person: Jude W. Shavlik
DBLP: Shavlik:Jude_W=
Facilitated 1 volumes:
EdContributed to:
2014
20112006
20042001200019961994199319911989Wrote 16 papers:
- SIGMOD-2014-GokhaleDDNRSZ #crowdsourcing #named
- Corleone: hands-off crowdsourcing for entity matching (CG, SD, AD, JFN, NR, JWS, XZ), pp. 601–612.
- VLDB-2011-NiuRDS #logic #markov #named #network #scalability #statistics #using
- Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (FN, CR, AD, JWS), pp. 373–384.
- VLDB-2006-ChenRST #analysis #predict
- Bellwether Analysis: Predicting Global Aggregates from Local Regions (BCC, RR, JWS, PT), pp. 655–666.
- KDD-2004-ShavlikS #detection #effectiveness #evaluation
- Selection, combination, and evaluation of effective software sensors for detecting abnormal computer usage (JWS, MS), pp. 276–285.
- ICML-2001-Eliassi-RadS #approach #information management
- A Theory-Refinement Approach to Information Extraction (TER, JWS), pp. 130–137.
- ICML-2000-CravenPSBG #coordination #learning #multi #using
- Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes (MC, DP, JWS, JB, JDG), pp. 199–206.
- KDD-1996-CherkauerS #information management
- Growing Simpler Decision Trees to Facilitate Knowledge Discovery (KJC, JWS), pp. 315–318.
- ICML-1994-CravenS #network #query #using
- Using Sampling and Queries to Extract Rules from Trained Neural Networks (MC, JWS), pp. 37–45.
- ICML-1994-OpitzS #knowledge-based #network #search-based #using
- Using Genetic Search to Refine Knowledge-based Neural Networks (DWO, JWS), pp. 208–216.
- ICML-1993-CravenS #learning #network #using
- Learning Symbolic Rules Using Artificial Neural Networks (MC, JWS), pp. 73–80.
- ML-1991-MaclinS #automaton #finite
- Refining Domain Theories Expressed as Finite-State Automata (RM, JWS), pp. 524–528.
- ML-1991-TowellCS #induction #knowledge-based #network
- Constructive Induction in Knowledge-Based Neural Networks (GGT, MC, JWS), pp. 213–217.
- ML-1989-FisherMMST #learning
- Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems (DHF, KBM, RJM, JWS, GGT), pp. 169–173.
- ML-1989-MaclinS
- Enriching Vocabularies by Generalizing Explanation Structures (RM, JWS), pp. 444–446.
- ML-1989-Shavlik #analysis #empirical #learning
- An Empirical Analysis of EBL Approaches for Learning Plan Schemata (JWS), pp. 183–187.
- ML-1989-ShavlikT #learning #network
- Combining Explanation-Based Learning and Artificial Neural Networks (JWS, GGT), pp. 90–93.