Two popular approaches for distributed training of SVMs on big data are parameter averaging and ADMM. Parameter averaging is efficient but suffers from loss of accuracy with increase in number of partitions, while ADMM in the feature space is accurate but suffers from slow convergence. In this paper, we report a hybrid approach called weighted parameter averaging (WPA), which optimizes the regu...

In this paper,we present a newpopulation-basedMonte Carlomethod, so-calledMOMCMC (Multi-Objective Markov Chain Monte Carlo), for sampling in the presence of multiple objective functions in real parameter space. The MOMCMC method is designed to address the ‘‘multi-objective sampling’’ problem, which is not only of interest in exploring diversified solutions at the Pareto optimal front in the fun...

The Hough transform is a voting scheme for locating geometric objects in point clouds. This paper describes its application for detecting lines in three dimensional point clouds. For parameter quantization, a recently proposed method for Hough parameter space regularization is used. The voting process is done in an iterative way by selecting the line with the most votes and removing the corresp...

Recent measurement of time dependent asymmetries in B → φK are indicative of a new source of CP violation. We examine squark mixing in SUSY as this new source, and using QCD improved factorization method to describe B → φK decay, find the allowed range of parameter space for ρ and ψ, the magnitude and phase of the down type LR(RL) squark mixing parameter δbs LR(RL). We then studyB → φK∗ and cal...