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Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. In earlier work, we selected extreme examples-those from the boundaries of induced concept descriptions-combined these with incoming instances, and used a batch learning algorithms to generate new concept descriptions. In this paper, we extend this work by...
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 Forgetting mechanisms also may be active to remove examples from partial memory that are irrelevant or outdated for the learning...
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