Abstract. We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data "resolution". Parameters of th...
Abstract— The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model gener...
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
We present a robust algorithm for independent component analysis that uses the sum of marginal quadratic negentropies as a dependence measure. It can handle arbitrary source densit...
: Kernel density estimation for multivariate data is an important technique that has a wide range of applications. However, it has received significantly less attention than its un...