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Abstract: . This paper describes a method for selecting training examples for a partial memory learning system. The method selects extreme examples that lie at the boundaries of concept descriptions and uses these examples with new training examples to induce new concept descriptions. Forgetting mechanisms also may be active to remove examples from partial memory that are irrelevant or outdated for the learning task. Using an implementation of the method, we conducted a lesion study and a direct... (Update)
Context of citations to this paper: More
.... be less susceptible to overtraining when learning concepts that change or drift, as compared to learners that use other memory models [13, 15]. The key issues for partial memory learning systems are how they select the most relevant examples from the input stream, maintain...
.... learning methods consists of algorithms that maintain past concept descriptions [1] or previously encountered examples [2] 3] [4]. Most if not all of these efforts have been directed at learning concepts that change or drift [5] If such learners successfully identify...
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BibTeX entry: (Update)
M.A. Maloof and R.S. Michalski. Selecting examples for partial memory learning. Machine Learning, 41:27--52, 2000. 37 http://citeseer.ist.psu.edu/maloof00selecting.html More
@article{ maloof00selecting,
author = "Marcus A. Maloof and Ryszard S. Michalski",
title = "Selecting Examples for Partial Memory Learning",
journal = "Machine Learning",
volume = "41",
number = "1",
pages = "27-52",
year = "2000",
url = "citeseer.ist.psu.edu/maloof00selecting.html" }
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