Chad M. Cumby and Dan Roth Department of Computer Science University of...  Home/Search
Context Related
| uiuc.edu/~danr/Papers/ilp02.ps.gz Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: uiuc.edu/~danr/publications (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: We present a paradigm for efficient learning and inference with relational data using propositional means. The paradigm utilizes description logics and concepts graphs in the service of learning relational models using efficient propositional learning algorithms. We introduce a Feature Description Logic (FDL) - a relational (frame based) language that supports efficient inference, along with a generation function that uses inference with descriptions in the FDL to produce features... (Update)
Cited by: More
Kernels on Prolog Proof Trees: Statistical Learning in .. - Passerini, Frasconi.. (2006) (Correct)
Active bibliography (related documents): More All
0.8: Relational Representations that Facilitate Learning - Cumby, Roth (2000) (Correct)
0.4: Relational Learning via Propositional Algorithms: An.. - Roth, Wen-Tau (2001) (Correct)
0.3: Research Summary 1997-2001 - Roth (Correct)
Similar documents based on text: More All
0.4: SNoW User Guide - Carlson, Cumby, Rosen, Roth (1999) (Correct)
0.1: Understanding Probabilistic Classifiers - Garg, Roth (2001) (Correct)
0.1: Randomization Tests for Relational Learning - Jensen, Neville, Rattigan (2003) (Correct)
BibTeX entry: (Update)
C. M. Cumby and D. Roth. Learning with feature description logics. In S. Matwin and C. Sammut, editors, Proceedings of the Twelfth International Conference on Inductive Logic Programming, volume 2583 of LNAI, pages 32--47. Springer-Verlag, 2002. http://citeseer.ist.psu.edu/cumby02learning.html More
@misc{ cumby02learning,
author = "C. Cumby and D. Roth",
title = "Learning with feature description logics",
text = "C. M. Cumby and D. Roth. Learning with feature description logics. In S.
Matwin and C. Sammut, editors, Proceedings of the Twelfth International
Conference on Inductive Logic Programming, volume 2583 of LNAI, pages 32--47.
Springer-Verlag, 2002.",
year = "2002",
url = "citeseer.ist.psu.edu/cumby02learning.html" }
Citations (may not include all citations):492 Learning logical definitions from relations (context) - Quinlan - 1990
317 Learning quickly when irrelevant attributes abound: A new li.. (context) - Littlestone - 1988
313 Inductive logic programming: Theory and methods - Muggleton, De Raedt - 1994
103 Learning probabilistic relational models - Friedman, Getoor et al. - 1999
92 Learning distributed representations of concepts (context) - Hinton - 1986
72 A fundamental tradeoff in knowledge representation and reaso.. (context) - Levesque, Brachman - 1985
51 A Winnow based approach to context-sensitive spelling correc.. - Golding, Roth - 1999
50 PAC-learnability of determinate logic programs - Dzeroski, Muggleton et al. - 1992
48 Learning boolean functions in an infinite attribute space - Blum - 1992
35 Cambridge Tracts in Theoretical Computer Science (context) - Carpenter, of et al. - 1992
34 Propositionalization approaches to relational data mining (context) - Kramer, Lavrac et al. - 2001
33 NP-completeness of the set unification and matching problmes (context) - Kapur, Narendran - 1986
28 Learnability of description logics with equality constraints - Cohen, Hirsh - 1994
28 Learning relations by pathfinding - Richards, Mooney - 1992
25 PAC-learning recursive logic programs: Negative result - Cohen - 1995
24 Tractable Default Reasoning (context) - Selman - 1990
23 PAC-learning recursive logic programs: Efficient algorithms - Cohen - 1995
21 Bayesian logic programs - Kersting, De Raedt - 2000
20 classic: A tractable probabilistic description logic (context) - Koller, Levy et al. - 1997
20 Automatic rule acquisition for spelling correction - Mangu, Brill - 1997
12 Relational learning with decision trees - Geibel, Wysotzki - 1996
12 Relational learning for NLP using linear threshold elements - Khardon, Roth et al. - 1999
9 Relational learning via propositional algorithms: An informa.. - Roth, Yih - 2001
7 Robust logic (context) - Valiant - 1999
6 Relational representations that facilitate learning - Cumby, Roth - 2000
5 Subsumption in KL-ONE is undecidable (context) - Schmidt-Schauss - 1989
4 Foundations of Logic Progamming (context) - Lloyd - 1987
3 Any-time relational reasoning: Resource-bounded induction an.. (context) - Sebag, Rouveirol
1 A semantics and complete algorithm for subsumption in the cl.. (context) - Patel-Schneider, Borgida - 1994
Documents on the same site (http://l2r.cs.uiuc.edu/~danr/publications.html): More
Defaults and Relevance in Model Based Reasoning - Khardon, Roth (1997) (Correct)
Learning to Reason with a Restricted View - Khardon, Roth (1995) (Correct)
Learning to Reason - Khardon (1994) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC