Many unsupervised learning algorithms make use of kernels that rely on the Euclidean distance between two samples. However, the Euclidean distance is optimal for Gaussian distribut...
Karim T. Abou-Moustafa, Mohak Shah, Fernando De la...
This paper introduces a novel statistical mixture model for probabilistic grouping of distributional histogram data. Adopting the Bayesian framework, we propose to perform anneale...
In this paper, a lapped orthogonal transform adapted for local directionality of an image is proposed and its application in adaptive image coding is presented. First, we address ...
We propose a new fibre tracking algorithm for cardiac DTMRI that parts with the locally "greedy" paradigm intrinsic to conventional tracking algorithms. We formulate the...
Semi-Supervised Support Vector Machines (S3 VMs) are an appealing method for using unlabeled data in classification: their objective function favors decision boundaries which do n...