—Probabilistic topic models were originally developed and utilised for document modeling and topic extraction in Information Retrieval. In this paper we describe a new approach f...
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside ...
Abstract--We explore the idea of applying machine learning techniques to automatically infer risk-adaptive policies to reconfigure a network security architecture when the context ...
The diameter k-clustering problem is the problem of partitioning a finite subset of Rd into k subsets called clusters such that the maximum diameter of the clusters is minimized. ...
Abstract. This paper studies photon-limited spectral intensity estimation and proposes a spatially and spectrally adaptive, nonparametric method for estimating spectral intensities...