Support vector machine (SVM) is a class of popular learning algorithms for good generalization. However, it is time-consuming in training SVM with a large set of samples. How to improve learning efficiency is one of the most important research tasks. It is known although there are many candidate training samples in learning tasks only the samples near decision boundary have influence on classif...

BACKGROUND A national multimedia campaign was launched in January 2010, to increase the proportion of young people tested for chlamydia. This study aimed to evaluate the impact of the campaign on the coverage and positivity within the National Chlamydia Screening Programme (NSCP) in England. METHOD An interrupted time series of anonymised NCSP testing reports for England for a 27 month period...

We introduce a new classification algorithm based on the concept of Symmetric Maximized Minimal distance in Subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, SMMS tries to identify a subspace in which all the authentic samples are close to each other and all the imposter samples are far away from the authentic samples. The optimality of the...

Stochastic billiards can be used for approximate sampling from the boundary of a bounded convex set through the Markov Chain Monte Carlo (MCMC) paradigm. This paper studies how many steps of the underlying Markov chain are required to get samples (approximately) from the uniform distribution on the boundary of the set, for sets with an upper bound on the curvature of the boundary. Our main theo...

Using as the stimulus a texture pattern of short lines, we compared positional acuity thresholds for an orientation-texture-defined (OTD) boundary and a luminance-defined (LD) boundary. Texture lines had different orientations but the same luminance on either side of the OTD boundary, and different luminances but the same orientation on either side of the LD boundary. For the LD boundary, both ...