Hirotaka Inoue

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Recently, multiple classifier systems (MCS) have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN) are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this(More)
Negative Correlation Learning (NCL) has been successfully applied to construct neu-ral network ensembles. It encourages the neural networks that compose the ensemble to be different from each other and, at the same time, accurate. The difference among the neural networks that compose an ensemble is a desirable feature to perform incremental learning, for(More)
We propose an efficient hybrid neural network for chaotic time series prediction. The hybrid neural network is constructed by a traditional feed-forward network, which is learned by using the backpropa-gation and a local model, which is implemented as a time delay embedding. The feed-forward network performs as the global approximation and the local model(More)
AIM The present study was a 52-week, non-comparative, open-label study of flexible dose paroxetine (20-40 mg) in 52 Japanese post-traumatic stress disorder (PTSD) patients in order to obtain clinical experience regarding efficacy and safety in regular clinical practice. METHODS Efficacy was measured using the Clinician-Administered PTSD Scale One Week(More)
Multiple classifier systems (MCS) have become popular during the last decade. Self-generating neural tree (SGNT) is one of the suitable base-classifiers for MCS because of the simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNT. In an earlier paper, we proposed a pruning method for the(More)