A key challenge for modern Bayesian statistics is how to perform scalable inference of pos- terior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Car
代价敏感支持向量机 A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge lo
With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a Graph signal processing (GSP)-based approach for nonintrusive
A fast descent algorithm, resorting to a "stretching" function technique and built on one hybrid method (GRSA) which combines simulated annealing (SA) algorithm and gradient based methods for large scale global optimizations, is proposed. Unlike the
We propose a total variation-based variational model for nonblind binary image deblurring. The binary constraint is considered using the double-well function as the penalty term. We show the existence of a minimizer for the proposed model. By using o
