Kernel mixture model for probability density estimation in Bayesian classifiers-Reference-Cited by-同舟云学术

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Kernel mixture model for probability density estimation in Bayesian classifiers

Published:2018-02-14 Issue:3 Volume:32 Page:675-707
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Zhang Wenyu,Zhang Zhenjiang,Chao Han-Chieh,Tseng Fan-Hsun

Funder

National Natural Science Foundation of China

Academic Discipline and Post-Graduate Education Project of the Beijing Municipal Commission of Education

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/article/10.1007/s10618-018-0550-5/fulltext.html

Reference44 articles.

1. Babich GA, Camps OI (1996) Weighted parzen windows for pattern classification. IEEE Trans Pattern Anal Mach Intell 18(5):567–570

2. Bielza C (2014) Discrete bayesian network classifiers: a survey. ACM Comput Surv 47(1):1–43

3. Bouckaert RR (2004) Naive bayes classifiers that perform well with continuous variables. In: AI 2004: advances in artificial intelligence, Springer, Berlin, pp 1089–1094

4. Castillo E, Gutierrez JM, Hadi AS (2012) Expert systems and probabilistic network models. Springer, Berlin

5. Chickering DM (2010) Learning bayesian networks is np-complete. Lect. Notes Stat. 112(2):121–130

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