Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is ...
We apply a scalable approach for practical, comprehensive design space evaluation and optimization. This approach combines design space sampling and statistical inference to ident...
We address the problem of unsupervised image auto-annotation with probabilistic latent space models. Unlike most previous works, which build latent space representations assuming ...
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original dist...
Abstract— Approximation techniques for labelled Markov processes on continuous state spaces were developed by Desharnais, Gupta, Jagadeesan and Panangaden. However, it has not be...