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English to Japanese: Bayesian network classifiers ベイジアンネットワーク識別器
Source text - English Bayesian network classifiers
N. Friedman, D. Geiger,
and M. Goldszmidt
Machine Learning 29:131--163, 1997.
Abstract
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with state-of-the-art classifiers such as C4.5.
This fact raises the question of whether a classifier with less restrictive assumptions can perform even better.
In this paper we evaluate approaches for inducing classifiers from data, based on the theory of learning Bayesian networks.
Bayesian networks are factored representations of probability distributions that generalize the naive Bayesian classifier and explicitly represent statements about independence.
Among these approaches we single out a method we call Tree Augmented Naive Bayes (TAN), which outperforms naive Bayes, yet at the same time maintains the computational simplicity (no search involved) and robustness that are characteristic of naive Bayes.
We experimentally tested these approaches, using benchmark problems from the University of California at Irvine repository, and compared them to C4.5, naive Bayes, and wrapper-based feature selection methods.
Translation - Japanese ベイジアンネットワーク識別器
N. フリードマン, D. ゲイジャー,
M. ゴールドスミス
機械学習 29:131--163, 1997.
Received Doctor of Science degree in physics from Tohoku University in 1987.
Worked for the Computer College of Iwasaki Gakuen from 1985 to 1991.
Worked for EXA Corp., IT company, from 1991 to 2005.
Published several scientific papers in English.
Translated software manuals, scientific papers, patent documents, etc.
Specialize in
Computers, IT, Engineering, Science, Mathematics & Statistics, Physics, etc.
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