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Abstract: Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause more or less radical changes in the concepts. Incremental concept learning in such domains requires the ability to recognize and adapt to such changes. This paper presents a solution for incremental learning tasks where the domain provides explicit clues as to the current context (e.g., attributes with characteristic values). We present a... (Update)
Context of citations to this paper: More
...changes in the concepts. Incremental concept learning in such domains requires the ability to recognize and adapt to such changes. Widmer (1996) presents a general two level learning model, and its realization in his METAL(B) system. This system can learn to detect certain...
...the data to fluctuate and make the previously discovered patterns partially invalid. Such phenomenon is termed as concept drift (Widmer, 1996). The possible solutions include incremental methods for updating the patterns and treating such drifts as an opportunity for...
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BibTeX entry: (Update)
Widmer, G. (1996). Recognition and exploitation of contextual clues via incremental metalearning. http://citeseer.ist.psu.edu/widmer96recognition.html More
@inproceedings{ widmer96recognition,
author = "Gerhard Widmer",
title = "Recognition and Exploitation of Contextual {CLues} via Incremental Meta-Learning",
booktitle = "International Conference on Machine Learning",
pages = "525-533",
year = "1996",
url = "citeseer.ist.psu.edu/widmer96recognition.html" }
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50 Semi-naive Bayesian classifier (context) - Kononenko - 1991
47 Incremental Learning from Noisy Data (context) - Schlimmer, Granger - 1986
42 Learning One More Thing - Thrun, Mitchell - 1995
36 Direct Transfer of Learned Information Among Neural Networks - Pratt, Mostow et al. - 1991
27 Induction of Recursive Bayesian Classifiers (context) - Langley - 1993
26 Beyond Incremental Processing: Tracking Concept Drift (context) - Schlimmer, Granger - 1986
25 Multitask Learning: A Knowledge-based Source of Inductive Bi.. - Caruana - 1993
17 How to Learn Imprecise Concepts: A Method Employing a Two-ti.. (context) - Michalski - 1987
16 Robust Classification with Context-Sensitive Features - Turney - 1993
12 Speaker Normalization and Adaptation Using Second-order Conn.. (context) - Watrous - 1993
9 Improving Shared Rules in Multiple Category Domain Theories - Ourston, Mooney - 1991
9 Learning Flexible Concepts from Streams of Examples: FLORA2 - Widmer, Kubat - 1992
9 Robust Classifiers Without Robust Features (context) - Katz, Gately et al. - 1990
9 Contextual Normalization Applied to Aircraft Gas Turbine Eng.. - Turney, Halasz - 1993
6 A Patient-adaptive Neural Network ECG Patient Monitoring Alg.. - Watrous, Towell - 1995
3 COBBIT - A Control Procedure for COBWEB in the Presence of C.. (context) - Computation, -- et al. - 1993
2 Instance-Based Learning (context) - Aha, Kibler et al. - 1991
2 Learning Two-tiered Descriptions of Flexible Contexts: The P.. (context) - Bergadano, Matwin et al. - 1992
2 Combining Robustness and Flexibility in Learning Drifting Co.. - Widmer - 1994
1 Effective Learning in Dynamic Environments by Explicit Conte.. (context) - UK, Widmer et al. - 1993
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