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This paper introduces a new learning algorithm for on-line ellipsoidal clustering. The algorithm is based on the competitive clustering scheme extended by two specific features. Elliptical clustering is accomplished by efficiently incorporating the Mahalanobis distance measure into the learning rules, and underutilization of smaller clusters is avoided by incorporating a frequency-sensitive term. Experiments are conducted to demonstrate the usefulness of the algorithm on artificial data-sets as well as on the problem of texture segmentation.

In this paper color image quantization by clustering is discussed. A clustering scheme, based on competitive learning is constructed and compared to the well-known C -means clustering algorithm. It is demonstrated that both perform equally well, but that the former is superior to the latter with respect to computing time. However, both depend on the initial conditions and may end up in local optima. Based on these findings, a hierarchical competitive learning scheme is constructed which is completely independent of initial conditions. The hierarchical approach is a hybrid structure between competitive learning and splitting of the color space. For comparison, a genetic approach is applied, which is a hybrid structure between a genetic algorithm and C -means clustering. The latter was demonstrated in the past to obtain global optimal results, but with high computational load. The hierarchical clustering scheme is shown to obtain near-global optimal results with low computational load.

In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data are added. To solve this problem, in this paper, we propose an incremental training method using one-class support vector machines. First, we generate a hypersphere for each class. Then, we keep data that exist near the boundary of the hypersphere as candidates for support vectors and delete others. By computer simulations for two-class and multiclass benchmark data sets, we show that we can delete data considerably without deteriorating the generalization ability.