We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
In this paper, we develop a probabilistic model for estimation of the numbers of cache misses during the sparse matrix-vector multiplication (for both general and symmetric matrice...
Hyperspectral imaging segmentation has been an active research area over the past few years. Despite the growing interest, some factors such as high spectrum variability are still...
Silvia Valero, Philippe Salembier, Jocelyn Chanuss...
We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/...
This paper develops a framework to support multiple protection strategies in optical networks, which is in general applicable to any connection-oriented network. The capacity avail...