Information separation via phasor neural networks and its application

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The research was performed to develop the artificial neural network models for explaining and resolving the mammalian brain function. We proposed the covariance field neural network model which is a natural extension of the classical analogue neural network model, and gives a mean field approximation to Markov random fields. The covariance field neural network can represent the covariance of spike timing as the phase difference, which is important in brain information processing, and can perform information processing based on spike timing. As a mean field approximation it gives much better approximation accuracy for Markov random fields even for the large weight strength compared with the naive mean field model. We performed computer experiments to support this. We also applied this model to image segmentation, and confirmed the segmentation capability with phase-difference. We proposed the mean field learning for Boltzmann machine, and performed some fundamental experiments to confirm the quick training speed for the phase.On the other hand, we proposed the efficient learning methods for neural netoworks, especially for the recently highlighted support vector machine(SVM). We extended SVM learning to the efficient multi-class algorithm, and apply the second order cone programming method to SVM learning. In addition we proposed the maximal margin classifier based on the geometric method, which behaves faster than the quick SVM known as SMO. Finally we proposed a new learning machine based on the kernel PCA, which can automatically determine the kernel parameter so that it can realize the no free parameter learning machine.