We propose an algorithm to construct classification models with a mixture of kernels from labeled and unlabeled data. The derived classifier is a mixture of models, each based o...
A technique for generating invariant polynomial inequalities of bounded degree is presented using the abstract interpretation framework. It is based on overapproximating basic semi...
Abstract. Training neural networks is a complex task of great importance in the supervised learning field of research. In this work we tackle this problem with five algorithms, a...
Abstract. Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of nonlinear dynamic systems. The Gaussian processes can h...
— This work presents a methodology for the application of a parallel genetic algorithm (PGA) to the problem of protein folding prediction, using the 3DHP-Side Chain model. This m...