In many classification applications, Support Vector Machines (SVMs) have proven to be highly performing and easy to handle classifiers with very good generalization abilities. Howe...
Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernel...
We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimi...
Abstract. This paper proposes a mathematical programming framew ork for combining SVMs with possibly di erent kernels. Compared to single SVMs, the advantage of this approach is tw...
— the paper discusses an approach of using traditional time series analysis, as domain knowledge, to help the data-preparation of support vector machine for classifying documents...
Ting Yu, Tony Jan, John K. Debenham, Simeon J. Sim...