Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
Kernel machines rely on an implicit mapping of the data such that non-linear classification in the original space corresponds to linear classification in the new space. As kernel ...
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Mac...
Image categorization is the problem of classifying images into one or more of several possible categories or classes, which are defined in advance. Classifiers can be trained usin...
In this paper we consider the problem of multi-object categorization. We present an algorithm that combines support vector machines with local features via a new class of Mercer k...
Barbara Caputo, Christian Wallraven, Maria-Elena N...