—In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the ...
In this paper, we present a method for object of interest detection. This method is statistical in nature and hinges in a model which combines salient features using a mixture of l...
We compute a common feature selection or kernel selection configuration for multiple support vector machines (SVMs) trained on different yet inter-related datasets. The method is ...
In this paper, we present a novel semidefinite programming approach for multiple-instance learning. We first formulate the multipleinstance learning as a combinatorial maximum marg...
This paper considers nonlinear modeling based on a limited amount of experimental data and a simulator built from prior knowledge. The problem of how to best incorporate the data ...