Symbolic kernel discriminant analysis

被引：0
|
作者：
Jean-Paul Rasson
Sandrine Lissoir
机构：
[1] University of Namur,Mathematical Department
来源：
Computational Statistics | 2000年 / 15卷
关键词：
Symbolic objects; training sets; prior and posterior probabilities; kernel density estimation; bayesian discrimination rule; EM-like algorithm;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Current technological progress in Hardware, Data Bases and Object Oriented languages implies the manipulation, stock and representation of objects with more and more complex data. The notion of Symbolic Objects is introduced on the base of Diday’s work and the necessity to be adapted to this notion appears for most recent classification methods. The aim of this paper is the adaptation of the classical Bayesian discrimination rule to the Symbolic Objects problematic. This will be performed by the prior probabilities’ estimation and by a kernel density estimation.
引用
收藏
页码：127 / 132
页数：5
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