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This paper describes a machine learning system that discovered a "negative motif", in transmembrane domain identification from amino acid sequences, and reports its experiments on protein data using P...IR database. We introduce a decision tree whose node are labeled with regular patterns. As a hypothesis, the system produces such decision tree for a small number of randomly chosen positive and negative examples from PIR. Experiments show that our system finds reasonable hypotheses very successfully. As a theoretical foundation, we show that the class of languages defined by decision trees of depth at most d over k-variable regular patterns is polynomial time learnable in the sense of probably approximately correct (PAC) learning for any fixed d, $ k gep 0 $.続きを見る
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Mendeley出力
rifis-tr-44