<テクニカルレポート>
A Machine Discovery from Amino Acid Sequences by Decision Trees over Regular Patterns

作成者
本文言語
出版者
発行日
雑誌名
出版タイプ
アクセス権
概要 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

本文情報を非表示

rifis-tr-44 pdf 1.11 MB 137  

詳細

レコードID
査読有無
関連情報
注記
タイプ
登録日 2009.04.22
更新日 2017.01.20