While support vector machines (SVMs) have shown great promise in supervised classification problems, researchers have had to rely on expert domain knowledge when choosing the SVM&...
We apply kernel-based machine learning methods to online learning situations, and look at the related requirement of reducing the complexity of the learnt classifier. Online meth...
This paper presents a novel method for DNA microarray gridding based on Support Vector Machine (SVM) classifiers. It employs a set of soft-margin SVMs to estimate the lines of the ...
Dimitris G. Bariamis, Dimitris Maroulis, Dimitrios...
Due to its wide applicability, the problem of semi-supervised classification is attracting increasing attention in machine learning. Semi-Supervised Support Vector Machines (S3VMs...
Olivier Chapelle, Vikas Sindhwani, S. Sathiya Keer...
Support vector machines (SVMs) are regularly used for classification of unbalanced data by weighting more heavily the error contribution from the rare class. This heuristic techn...