Classification of fluorescence in situ hybridization images using belief networks

被引：16

作者：

Malka, R ^{[1
]}

Lerner, B ^{[1
]}

机构：

[1] Ben Gurion Univ Negev, Dept Elect & Comp Engn, Pattern Anal & Machine Learning Lab, IL-84105 Beer Sheva, Israel

来源：

PATTERN RECOGNITION LETTERS | 2004年 / 25卷 / 16期

关键词：

belief networks; fluorescence in situ hybridization (FISH); image classification; naive Bayesian classifier; K2; algorithm;

D O I：

10.1016/j.patrec.2004.07.010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The structure and parameters of a belief network are learned in order to classify images enabling the detection of genetic abnormalities. We compare a structure learned from the data to another structure obtained utilizing expert knowledge and to the naive Bayesian classifier and study quantization in comparison to density estimation in parameter learning. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：1777 / 1785

页数：9

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