In this paper, we present a novel entropy estimator for a given set of samples drawn from an unknown probability density function (PDF). Counter to other entropy estimators, the e...
We present a novel framework for multi-label learning that explicitly addresses the challenge arising from the large number of classes and a small size of training data. The key a...
Online adaptation is a key requirement for image processing applications when used in dynamic environments. In contrast to batch learning, where retraining is required each time a...
Classical methods for solving numerical CSPs are based on a branch and prune algorithm, a dichotomic enumeration process interleaved with a consistency filtering algorithm. In man...
not use pointer arithmetic. Such "pure pointer algorithms" thus are a useful abstraction for studying the nature of logspace-computation. In this paper we introduce a for...