Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
We evaluate the impact of a gigabit network on the implementation of a distributed chemical process optimization application. The optimization problem is formulated as a stochasti...
Abstract. This paper presents Artificial Neural Network (ANN) based architecture for underwater object detection from Light Detection And Ranging (Lidar) data. Lidar gives a sequen...
The notion of algorithmic stability has been used effectively in the past to derive tight generalization bounds. A key advantage of these bounds is that they are designed for spec...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...