I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
This paper pertains to the recognition of textural regions for color video analysis. The proposed scheme uses the covariance of 2nd -order statistics on the wavelet domain, betwee...
Dimitrios K. Iakovidis, Dimitrios E. Maroulis, S. ...
The Support Vector Machine (SVM) is a powerful tool for classification. We generalize SVM to work with data objects that are naturally understood to be lying on curved manifolds, ...
Suman K. Sen, Mark Foskey, James Stephen Marron, M...
In this paper, we provide a study on the use of tree kernels to encode syntactic parsing information in natural language learning. In particular, we propose a new convolution kerne...
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors su...