Augmented Efficient BackProp for Backpropagation Learning in Deep Autoassociative Neural Networks

被引：0

作者：

Embrechts, Mark J. ^{[1
]}

Hargis, Blake J. ^{[1
]}

Linton, Jonathan D. ^{[2
]}

机构：

[1] Rensselaer Polytech Inst, Dept Ind & Syst Engn, Troy, NY 12180 USA

[2] Univ Ottawa, Sch Management, Ottawa, ON K1N 6N5, Canada

来源：

2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年

基金：

加拿大自然科学与工程研究理事会;

关键词：

PRINCIPAL COMPONENT ANALYSIS; NONLINEAR PCA; ALGORITHM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce Augmented Efficient BackProp as a strategy for applying the backpropagation algorithm to deep autoencoders, i.e., autoassociators with many hidden layers, without relying on a weight initialization using restricted Boltzmann machines. This training method is an extension of Efficient BackProp, first proposed by LeCun et al. [1], and is benchmarked on three different types of application datasets.

引用

页数：6

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