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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク First Order Inductive Learner ： ウィキペディア英語版 First Order Inductive Learner In machine learning, First Order Inductive Learner (FOIL) is a rule-based learning algorithm. ==Background== Developed in 1990 by Ross Quinlan,〔J.R. Quinlan. Learning Logical Definitions from Relations. Machine Learning, Volume 5, Number 3, 1990. ()〕 FOIL learns function-free Horn clauses, a subset of first-order predicate calculus. Given positive and negative examples of some concept and a set of background-knowledge predicates, FOIL inductively generates a logical concept definition or rule for the concept. The induced rule must not involve any constants (''color(X,red)'' becomes ''color(X,Y), red(Y)'') or function symbols, but may allow negated predicates; recursive concepts are also learnable. Like the ID3 algorithm, FOIL hill climbs using a metric based on information theory to construct a rule that covers the data. Unlike ID3, however, FOIL uses a separate-and-conquer method rather than divide-and-conquer, focusing on creating one rule at a time and collecting uncovered examples for the next iteration of the algorithm. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「First Order Inductive Learner」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.