Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the...
This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and...
— Results of queries by personal names often contain documents related to several people because of the namesake problem. In order to differentiate documents related to different...
A neural architecture is presented that encodes the visual space inside and outside of a shape. The contours of a shape are propagated across an excitable neuronal map and fed thro...
This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, loglinear models without mixtures have been u...