phi-Divergence Loss-Based Artificial Neural Network

被引：0

作者：

Salamwade, R. L. ^{[1
]}

Sakate, D. M. ^{[1
]}

Mathur, S. K. ^{[2
]}

机构：

[1] Shivaji Univ, Kolhapur, Maharashtra, India

[2] Augusta Univ, Augusta, GA USA

来源：

JOURNAL OF MODERN APPLIED STATISTICAL METHODS | 2018年 / 17卷 / 02期

关键词：

Power divergence family; classification; back-propagation; loss function; mean square error;

D O I：

10.22237/jmasm/1551966252

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Artificial Neural Networks (ANNs) can fit non-linear functions and recognize patterns better than several standard techniques. Performance of ANNs is measured by using loss functions. Phi-divergence estimator is generalization of maximum likelihood estimator and it possesses all its properties. A neural network is proposed which is trained using phidivergence loss.

引用

页数：13

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