This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
The present work studies clustering from an abstract point of view and investigates its properties in the framework of inductive inference. Any class S considered is given by a hyp...
John Case, Sanjay Jain, Eric Martin, Arun Sharma, ...
We introduce a mixture of probabilistic canonical correlation analyzers model for analyzing local correlations, or more generally mutual statistical dependencies, in cooccurring d...
Heuristic measures for estimating the quality of attributes mostly assume the independence of attributes so in domains with strong dependencies between attributes their performanc...
Leading classification methods such as support vector machines (SVMs) and their counterparts achieve strong generalization performance by maximizing the margin of separation betw...