Eigenvalue decay: A new method for neural network regularization

NEUROCOMPUTING

卷:124

Article

Ludwig, Oswaldo¹  Nunes, Urbano¹  Araujo, Rui¹ 

[1] Univ Coimbra, ISR Inst Syst & Robot, P-3000 Coimbra, Portugal

关键词: Transduction;    Regularization;    Genetic algorithm;    Classification margin;    Neural network;   

DOI  :  10.1016/j.neucom.2013.08.005

来源: Elsevier

【 摘 要 】

This paper proposes two new training algorithms for multilayer perceptrons based on evolutionary computation, regularization, and transduction. Regularization is a commonly used technique for preventing the learning algorithm from overfitting the training data. In this context, this work introduces and analyzes a novel regularization scheme for neural networks (NNs) named eigenvalue decay, which aims at improving the classification margin. The introduction of eigenvalue decay led to the development of a new training method based on the same principles of SVM, and so named Support Vector NN (SVNN). Finally, by analogy with the transductive SVM (TSVM), it is proposed a transductive NN (TNN), by exploiting SVNN in order to address transductive learning. The effectiveness of the proposed algorithms is evaluated on seven benchmark datasets. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

Free