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Abstract: Most research on supervised learning assumes the attributes of training and test examples are completely specified. Real-world data, however, is often incomplete. This paper studies the task of learning to classify incomplete test examples, given incomplete (resp., complete) training data. We first show that the performance task of classifying incomplete examples requires the use of default classification functions which demonstrate nonmonotonic classification behavior. We then extend the... (Update)
Context of citations to this paper: More
.... for the classification task, including neural networks [37] genetic algorithms [12] inductive and instancebased learning [1, 28, 37, 40, 36] and case based reasoning [31, 4, 15] Individual approaches are compared to each other based on the method they deploy, the...
...As noted above (and elsewhere throughout the learning and data mining communities) real world data is usually incomplete. The papers [27, 23] address this discrepancy by formally analyzing the task of learning to classify incompletely specified performance examples...
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8: DNF with noise in the attributes (context) - Shackelford, Volper - 1988
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BibTeX entry: (Update)
Dale Schuurmans and Russell Greiner. Learning to classify incomplete examples. In Fourth Annual Workshop on Computational Learning Theory and `Natural' Learning Systems (CLNL93), Provincetown MA, 1993. http://citeseer.ist.psu.edu/schuurmans93learning.html More
@inbook{ schuurmans97learning,
author = "Dale Schuurmans and Russell Greiner",
title = "Learning to Classify Incomplete Examples",
booktitle = "Computational Learning Theory and Natural Learning Systems {IV}: Making Learning Systems Practical",
publisher = "MIT Press",
pages = "87--105",
year = "1997",
url = "citeseer.ist.psu.edu/schuurmans93learning.html" }
Citations (may not include all citations):1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
760 Probabilistic Reasoning in Intelligent Systems (context) - Pearl - 1988
537 A theory of the learnable (context) - Valiant - 1984
465 Learnability and the Vapnik-Chervonenkis dimension (context) - Blumer, Ehrenfeucht et al. - 1989
454 the uniform convergence of relative frequencies of events to.. (context) - Vapnik, Chervonenkis - 1971
268 Decision theoretic generalizations of the PAC model for neur.. (context) - Haussler - 1992
203 Statistical Analysis with Missing Data (context) - Little, Rubin - 1987
151 A general lower bound on the number of examples needed for l.. (context) - Ehrenfeucht, Haussler et al. - 1988
149 Heuristic classification (context) - Clancey - 1985
142 Learning from noisy examples (context) - Angluin, Laird - 1988
115 Efficient distribution-free learning of probabilistic concep.. - Kearns, Schapire - 1990
111 Connectionist learning of belief networks (context) - Neal - 1992
102 Readings in Nonmonotonic Reasoning (context) - Ginsberg - 1987
96 The need for biases in learning generalizations - Mitchell - 1980
94 Learning in the presence of malicious errors - Kearns, Li - 1988
80 Concept learning and heuristic classification in weak-theory.. - Porter, Bareiss et al. - 1990
62 the hardness of approximate reasoning - Roth - 1993
59 Unknown attribute values in induction - Quinlan - 1989
42 Nonmonotonic reasoning (context) - Reiter - 1987
28 DNF with noise in the attributes (context) - Shackelford, Volper - 1988
21 Learning default concepts - Schuurmans, Greiner - 1994
18 Learning complicated concepts reliably and usefully (context) - Rivest, Sloan - 1988
9 Evidential probability (context) - Kyburg - 1991
2 Exploiting the absence of irrelevant information (context) - Rao, Greiner et al. - 1994
1 Supervised learning from real and discrete incomplete data (context) - Ghahramani, Jordan - 1994
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