Using evolutionary programming and minimum description length principle for data mining of Bayesian networks
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摘要We have developed a new approach (MDLEP) to learning Bayesian network structures based on the Minimum Description Length (MDL) principle and Evolutionary Programming (EP). It employs a MDL metric, which is founded on information theory, and integrates a knowledge-guided genetic operator for the optimization in the search process.
著者Wong ML, Lam W, Leung KS
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence
出版年份1999
月份2
日期1
卷號21
期次2
出版社IEEE COMPUTER SOC
頁次174 - 178
國際標準期刊號0162-8828
電子國際標準期刊號1939-3539
語言英式英語
關鍵詞Bayesian networks; evolutionary computation; genetic algorithms; minimum description length principle; unsupervised learning
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; Engineering; Engineering, Electrical & Electronic; ENGINEERING, ELECTRICAL & ELECTRONIC

上次更新時間 2021-23-02 於 00:55