This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the stati...
We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimi...
A methodology is described for synthesizing signal processing networks, which are used to solve a low-cost medical signal processing problem. The approach makes use of genetic alg...
We present a heuristic approach to the geometric motion planning problem with the aim to quickly solve intuitively simple problems. It is based on a divide-and-conquer path search...
We present a new approach to preconditioning for very large, sparse, non-symmetric, linear systems. We explicitly compute an approximate inverse to our original matrix that can be...