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A fully automatic method for breast cancer diagnosis based on microscopic biopsy images is presented. The method achieves high recognition rates by applying multi-class support vector machines on generic feature vectors that are based on level-set statistics of the images. We also consider the problem of classification with rejection and show preliminary(More)
Hand amputees would highly benefit from a robotic prosthesis, which would allow the movement of a number of fingers. In this paper we propose using the electromyographic signals recorded by two pairs of electrodes placed over the arm for operating such prosthesis. Multiple features from these signals are extracted whence the most relevant features are(More)
A new sparsity driven kernel classifier is presented based on the minimization of a recently derived data-dependent generalization error bound. The objective function consists of the usual hinge loss function penalizing training errors and a concave penalty function of the expansion coefficients. The problem of minimizing the non-convex bound is addressed(More)
A novel linear feature selection algorithm is presented based on the global minimization of a data-dependent generalization error bound. Feature selection and scaling algorithms often lead to non-convex optimization problems, which in many previous approaches were addressed through gradient descent procedures that can only guarantee convergence to a local(More)
This paper concerns some of the theoretical complexity aspects of the reconfigurable network model. The computational power of the model is investigated under several variants, depending on the type of switches (or switch operations) assumed by the network nodes. Computational power is evaluated by focusing on the set of problems computable in constant time(More)
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