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This paper concerns the computational aspects of the reconngurable network model. The computational power of the model is investigated under several network topologies and assuming several variants of the model. In particular, it is shown that there are reconngurable machines based on simple network topologies, that are capable of solving large classes of(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 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)
Sparsity plays an important role in many fields of engineering. The cardinality penalty function, often used as a measure of sparsity, is neither continuous nor differentiable and therefore smooth optimization algorithms cannot be applied directly. In this paper we present a continuous yet non-differentiable sparsity function which constitutes a tight lower(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)
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)
Objective. The aim of this study was to test whether pattern recognition classifiers with multiple clinical and sonographic variables could improve ultrasound prediction of fetal macrosomia over prediction which relies on the commonly used formulas for the sonographic estimation of fetal weight. Study design. The SVM algorithm was used for binary(More)
T h i s p a p e r c o n c e r n s s o m e o f t h e t h e o r e t i c a l c o m p l e x i t y a s p e c t s o f t h e r e c o n f i g u r a b l e n e t w o r k m o d e l . T h e c o m p u t a t i o n a l p o w e r o f t h e m o d e l i s i n v e s t i g a t e d u n d e r s e v e r a l v a r i a n t s , d e p e n d i n g o n t h e t y p e o f s w i t c h e s(More)