Probability density estimation using adaptive activation function neurons

被引：16

作者：

Fiori, S

Bucciarelli, P

机构：

[1] Univ Perugia, Neural Networks & Adapt Syst Res Grp, Dept Ind Engn, Perugia, Italy

[2] Univ Ulm, Zent Inst Biomed Tech, Ulm, Germany

来源：

NEURAL PROCESSING LETTERS | 2001年 / 13卷 / 01期

关键词：

adaptive activation function neurons; cumulative distribution function; differential entropy; probability density function; stochastic gradient;

D O I：

10.1023/A:1009635129159

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we deal with the problem of approximating the probability density function of a signal by means of adaptive activation function neurons. We compare the proposed approach to the one based on a mixture of kernels and show through computer simulations that comparable results may be obtained with limited expense in computational efforts.

引用

页码：31 / 42

页数：12

共 20 条

[1]

[Anonymous], ADAPTIVE PATTERN REC

[2]

ARAM Y, 1994, CIS9420 ISR I TECHN

[3] Efficient minimisation of the KL distance for the approximation of posterior conditional probabilities [J].