In many real world applications, active selection of training examples can significantly reduce the number of labelled training examples to learn a classification function. Differ...
We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike...
The distribution of the apparent 3D shape of human faces across the view-sphere is complex, owing to factors such as variations in identity, facial expression, minor occlusions an...
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-...
We present three enhancements to accelerate the extraction of separatrices of three-dimensional vector fields, using intelligently selected “sample” streamlines. These enhanc...