Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Semi-supervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likeliho...
Joshua Dillon, Krishnakumar Balasubramanian, Guy L...
We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we ...
We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to loca...
In this paper a real-time anomaly detection system for video streams is proposed. Spatio-temporal features are exploited to capture scene dynamic statistics together with appearan...