By Kokichi Tanaka (auth.), K. S. Fu (eds.)
This publication comprises the court cases of the US-Japan Seminar on studying procedure up to speed structures. The seminar, held in Nagoya, Japan, from August 18 to twenty, 1970, was once subsidized via the US-Japan Cooperative technology application, together supported via the nationwide technological know-how origin and the Japan Society for the merchandising of technological know-how. the complete texts of all of the awarded papers other than t are incorporated. The papers conceal an outstanding number of subject matters with regards to studying tactics and platforms, starting from development popularity to structures id, from studying regulate to organic modelling. with the intention to replicate the particular content material of the ebook, the current name was once chosen. the entire twenty-eight papers are approximately divided into parts--Pattern reputation and method identity and studying method and studying regulate. it's occasionally particularly seen that a few papers might be categorised into both half. the alternative in those situations was once strictly the editor's which will retain a definite stability among the 2 components. in the past decade there was a substantial progress of curiosity in difficulties of trend popularity and computing device research ing. In designing an optimum development reputation or keep watch over method, if all of the a priori information regarding the method less than research is understood and will be defined deterministically, the optimum procedure is generally designed by way of deterministic optimization techniques.
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Additional info for Pattern Recognition and Machine Learning: Proceedings of the Japan—U.S. Seminar on the Learning Process in Control Systems, held in Nagoya, Japan August 18–20, 1970
The threshold effect of an optimal (LMS) nonlinear algorithm was first derived by Rozov and Chelpanov  in connection with highly distorted signals characterized by univariate Gaussian noise. The nonlinear algorithm derived in this paper is essentially an extension of Rozov and Chelpanov's algorithm. Also related are problems of detecting outlying samples in a statistical population. Karlin and Truax  examined the nature of this problem from the Bayesian viewpoint and showed that the Bayes solution was also characterized by a threshold effect.
In the context of pattern recognition and learning, extremely noisy samples detected by this algorithm are not to be discarded altogether from the learning population but are used to modify the unknown parameters with a vector of constant norm, regardless of the actual measurements. Albert and Gardner  have discussed the convergence property of this type of algorithms and several theorems have been conjectured. Further studies, however, would have to be made in regard to its learning performance so that the effect of a clipping can be compared with that of a threshold discussed in this paper.
Thus, in the case of nonsupervised learning, the following questions arise naturally. How many steady-state solutions will the algorithm yield? 29 30 MORISHITA AND TAKANUKI With what type of classification is each solution associated? If we wish to answer to these ~uestions, we must investigate the steadystate behavior of the algorithm. In the algorithm proposed, weight vectors are represented as time functions and the rule for their adjustments is described by a differential e~uation. First, on the basis of the assumption that patterns in the input se~uence are those obtained from a distribution by random sampling, analytical techni~ues are developed for finding the steady-state solutions of the differential e~uation and for determining the stability of each solution found.
Pattern Recognition and Machine Learning: Proceedings of the Japan—U.S. Seminar on the Learning Process in Control Systems, held in Nagoya, Japan August 18–20, 1970 by Kokichi Tanaka (auth.), K. S. Fu (eds.)