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2007
Springer

Solving Selected Classification Problems in Bioinformatics Using Multilayer Neural Network Based on Multi-Valued Neurons (MLMVN)

10 years 6 months ago
Solving Selected Classification Problems in Bioinformatics Using Multilayer Neural Network Based on Multi-Valued Neurons (MLMVN)
A multilayer neural network based on multi-valued neurons (MLMVN) is a new powerful tool for solving classification, recognition and prediction problems. This network has a number of specific properties and advantages that follow from the nature of a multi-valued neuron (complexvalued weights and inputs/outputs lying on the unit circle). Its backpropagation learning algorithm is derivative-free. The learning process converges very quickly, and the learning rate for all neurons is self-adaptive. The functionality of the MLMVN is higher than the one of the traditional feedforward neural networks and a variety of kernel-based networks. Its higher flexibility and faster adaptation to the mapping implemented make it possible to solve complex classification problems using a simpler network. In this paper, we show that the MLMVN can be successfully used for solving two selected classification problems in bioinformatics.
Igor N. Aizenberg, Jacek M. Zurada
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2007
Where ICANN
Authors Igor N. Aizenberg, Jacek M. Zurada
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