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 ...
The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the featu...
This paper concerns the design of a Support Vector Machine (SVM) appropriate for the learning of Boolean functions. This is motivated by the need of a more sophisticated algorithm ...
In this paper we address the two-class classification problem within the tensor-based framework, by formulating the Support Tucker Machines (STuMs). More precisely, in the propos...
The Support Vector Machine (SVM) is a powerful tool for classification. We generalize SVM to work with data objects that are naturally understood to be lying on curved manifolds, ...
Suman K. Sen, Mark Foskey, James Stephen Marron, M...