A statistical approach to decision tree modeling
作者： Michael I. Jordan
关键词: Artificial intelligence 、 Incremental decision tree 、 Mathematics 、 Machine learning 、 Logistic model tree 、 Decision tree 、 Tree (data structure) 、 Decision tree learning 、 Data mining 、 Decision tree model 、 Order statistic tree 、 ID3 algorithm
摘要: A statistical approach to decision tree modeling is described. In this approach, each in the modeled parametrically as process by which an output generated from input and a sequence of decisions. The resulting model yields likelihood measure goodness fit, allowing ML MAP estimation techniques be utilized. An efficient algorithm presented estimate parameters tree. selection problem several alternative proposals are considered. hidden Markov version described for data sequences that have temporal dependencies.
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