A competitive learning process for hyperspherical classifiers. - Yen, Chen-Wen V. (Neurocomputing :an international journal.)
Based on the minimum cost associative processor training algorithm proposed by Telfer and Casasent, this paper introduces a new training method for hyperspherical classifiers. Designed specifically for the hyperspherical neural networks, a novel feature of the proposed approach is a clustering method which can simplify the classification problem by adaptively removing redundant samples from the training set. The other distinct feature of the proposed method is the use of an adaptive hypersphere placement method. Specifically, in constructing a new hypersphere, a candidate hypersphere is generated for each class of the training samples. The hypersphere that has successfully classified the largest number of samples is selected as the new hypersphere. As demonstrated by the experimental results, the proposed method can achieve better classification accuracy with less training time and fewer number of hyperspheres. The proposed approach can also solve some difficult classification problems which can not be effectively handled by the previous method.