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Abstract: In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the featurevector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the setvalued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. ... (Update)
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0.5: The Generality Of Overgenerality - Cohen (Correct)
0.3: Context-Sensitive Learning Methods for Text Categorization - Cohen, Singer (1996) (Correct)
0.2: Learning Rules that Classify E-Mail - Cohen (1996) (Correct)
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BibTeX entry: (Update)
Cohen, W. W. 1996. Learning trees and rules with setvalued features. In Proceedings of the 13th National Conference on Artificial Intelligene (AAAI-96), 709-- 716. AAAI Press. http://citeseer.ist.psu.edu/article/cohen96learning.html More
@inproceedings{ cohen96learning,
author = "William W. Cohen",
title = "Learning Trees and Rules with Set-Valued Features",
booktitle = "{AAAI}/{IAAI}, Vol. 1",
pages = "709-716",
year = "1996",
url = "citeseer.ist.psu.edu/article/cohen96learning.html" }
Citations (may not include all citations):1359 Induction of decision trees (context) - Quinlan - 1990 ACM DBLP
317 Learning quickly when irrelevant attributes abound: A new li.. (context) - Littlestone - 1988 DBLP
307 Information Retrieval - Van Rijsbergen - 1979 ACM DBLP
281 programs for machine learning (context) - Quinlan - 1994
248 Fast effective rule induction - Cohen - 1995 DBLP
248 An introduction to computational learning theory (context) - Kearns, Vazarani - 1994 ACM
233 The CN2 induction algorithm - Clark, Niblett - 1989 ACM DBLP
212 Inductive Logic Programming: Techniques and Applications (context) - Lavrac, Dzeroski - 1994
149 New Generation Computing (context) - Muggleton, Progol - 1995
110 Context-sensitive learning methods for text categorization - Cohen, Singer - 1996 ACM DBLP
97 A comparison of two learning algorithms for text categorizat.. - Lewis, Ringuette - 1994
75 Heterogeneous uncertainty sampling for supervised learning - Lewis, Catlett - 1994
49 Learning the CLASSIC description logic: Theoretical and expe.. - Cohen, Hirsh - 1994 DBLP
48 Learning boolean functions in an infinite attribute space - Blum - 1992 ACM DBLP
47 Megainduction: a test flight (context) - Catlett - 1991 DBLP
46 Learning logical definitions from relations (context) - Quinlan - 1990 ACM DBLP
44 Generalized subsumption and its application to induction and.. (context) - Buntine - 1988
41 Automated learning of decision rules for text categorization (context) - Apt'e, Damerau et al. - 1994 ACM DBLP
41 Classification and Regression Trees (context) - Brieman, Friedman et al. - 1984
33 Text categorization and relational learning - Cohen - 1995 DBLP
27 Text categorization of low quality images (context) - Ittner, Lewis et al. - 1995
24 HYDRA: A noisetolerant relational concept learning algorithm - Ali, Pazzani - 1993
22 Detecting and correcting errors in rule-based expert systems.. - Pazzani, Brunk - 1991
20 Training text classifiers by uncertainty sampling (context) - Lewis, Gale - 1994
18 Pac-learning nondeterminate clauses - Cohen - 1994 ACM DBLP
18 Rapid prototyping of ILP systems using explicit bias - Cohen - 1993
11 Background knowledge and declarative bias in inductive conce.. (context) - Lavrac, Dzeroski - 1992 ACM DBLP
9 Computer Science Dept (context) - Lewis, learning et al. - 1992
7 A bootstrapping approach to conceptual clustering (context) - Morik - 1989 ACM
2 Creating a Memory of Causal Relationships (context) - Pazzani - 1990 ACM
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.research.att.com/~wcohen/ripperd.html):
Fast Effective Rule Induction - Cohen (1995) (Correct)
Learning Rules that Classify E-Mail - Cohen (1996) (Correct)
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