Nobuhiko Yamaguchi

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There are many cases that a neural-network-based system must memorize some new patterns incrementally. However, if the network learns the new patterns only by referring to them, it probably forgets old memorized patterns, since parameters in the network usually correlate not only to the old memories but also to the new patterns. A certain way to avoid the(More)
The k-nearest neighbor (KNN) classification is a simple and effective classification approach. However, improving performance of the classifier is still attractive. Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding that(More)
Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced by Bishop et al. as a data visualization technique. In this paper, we focus on variational Bayesian inference for the GTM. The variational Bayesian GTM was first proposed by Olier et al. However, the GTM of Olier et al. uses a single regularization term and regularization(More)
This paper proposes a low-cost incremental learning method of Generalized Radial Basis Function (GRBF) for a Case Based Reasoning (CBR) system. A CBR system is one type of reasoning system that uses past cases for solving new problems. To realize the reasoning, the system has to search a past case which is similar to the new problem from a case database. If(More)