Abstract-This paper explores the possibility of constructing RBF classifiers which, somewhat like support vector machines, use a reduced number of samples as centroids, by means of selecting samples in a direct way. Because sample selection is viewed as a hard computational problem, this selection is done after a previous vector quantization: this way obtaining also other similar machines using centroids selected from those that are learned in a supervised manner. Several forms of designing these machines are considered, in particular with respect to sample selection; as well as some different criteria to train them. Simulation results for well-known classification problems show very good performance of the corresponding designs, improving that of support vector machines and reducing substantially their number of units. This shows that our interest in selecting samples (or centroids) in an efficient manner is justified. Many new research avenues appear from these experiments and discussions, as suggested in our conclusions.

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The common approach to image matching is to detect spatial features present in both images and create a mapping that relates both images. The main drawback of this method takes place when more than one matching is likely. A first simplification to this ambiguity is to represent with a parametric model the point locus where the matching is highly likely, and then use a POCS (projection onto convex sets) procedure combined with Tikhonov regularization that results in the mapping vectors. However, if there is more than one model per pixel, the regularization and constraintforcing process faces a multiple-choice dilemma that has no easy solution. This work proposes a framework to overcome this drawback: the combined projection over multiple models based on the L k norm of the projection-point distance. This approach is tested on a stereo-pair that presents multiple choices of similar likelihood.

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Child-to-parent violence assessment has raised much concern in the last decade. The Child-to-Parent Violence Risk (CPVR) assessment tool is a recently developed guide, designed to anticipate violence recidivism, that can be used during therapy, pretrial assessment, and other circumstances were professionals need support to determinate needs and risks of cases. This study aimed to provide empirical data on the use of the CPVR in a therapeutic context, describing the prevalence of risk factors of youth attending a cognitive-behavioral program, comparing scores on CPVR in a pre-post assessment, and analyzing its ability to predict treatment results. A total of 118 youths were assessed using the CPVR before treatment, and 66 also had a post-treatment assessment. Significant changes in risk (reduction) and protective (increase) factors after program participation (due to the program or due to the professional’s consideration in post-treatment assessment) were observed, but the CPVR was not able to predict the success coded by clinicians. Future research should include recidivism data to confirm the real success after the treatment program (regardless of the professional’s opinion) and the predictive validity of the CPVR for recidivism.