A case study of incremental concept induction
作者： Jeffrey C. Schlimmer , Douglas Fisher
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摘要: Application of machine induction techniques in complex domains promises to push the computational limits non-incremental, search intensive methods. Learning effectiveness requires development incremental, cost effective However, discussion dimensions for comparing utility differing incremental methods has been lacking. In this paper we introduce 3 characterizing concept systems which relate and quality learning. The are used compare respective merits 4 variants Quinlan's learning from examples program, ID3. This comparison indicates that can be obtained, without significantly detracting induced knowledge.
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