MLMVN With Soft Margins Learning

doi:10.1109/TNNLS.2014.2301802

2014-09-01

PRE

Igor Aizenberg *

DOI：10.1109/TNNLS.2014.2301802

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摘要

En 中文

In this paper, we consider a modified error-correction learning rule for the multilayer neural network with multivalued neurons (MLMVN). This modification is based on the soft margins technique, which leads to the minimization of the distance between a cluster center and the learning samples belonging to this cluster. MLMVN has a derivative-free learning algorithm based on the error-correction learning rule and demonstrate a higher functionality and better generalization capability than a number of other machine learning techniques. The discrete k-valued multivalued neuron activation function divides a complex plane into k equal sectors. For more efficient and reliable solving of classification problems it is possible to modify the MLMVN learning algorithm in such a way that learning samples belonging to different classes (clusters) will be located as close as possible to the bisector of a desired sector (the cluster center) and as far as possible from each other, respectively. Such a modification based on the soft margins learning technique is considered in this paper. This modified learning algorithm improves the generalization capability of MLMVN when solving classification problems.

Keyword:

Classification

complex-valued neural networks (CVNNs)

multilayer neural network with multivalued neuron (MLMVN)

MVN

soft margins

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期刊

IEEE Transactions on Neural Networks and Learning Systems

IF:

8.9

论文数:

7.6K

被引数:

7.2W

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