A least square kernel machine with box constraints

In this paper, we present a least square kernel machine with box constraints (LSKMBC). The existing least square machines assume Gaussian hyperpriors and subsequently express the optima of the regularized squared loss as a set of linear equations. The generalized LASSO framework deviates from the assumption of Gaussian hyperpriors and employs a more general Huber loss function. In our approach, we consider uniform priors and obtain the loss functional for a given margin considered to be a model selection parameter. The framework not only differs from the existing least square kernel machines, but also it does not require Mercer condition satisfiability. Experimentally we validate the performance of the classifier and show that it is able to outperform SVM and LSSVM on certain real-life datasets.