In this paper, we introduce a novel method, Multi − Class Sparse Bayesian Regression(MCBR), that generalizes classical approaches such as Ridge regression and ...

Our approach is based on a grouping of the features into several classes, where each class is regularized with specific parameters. We apply our algorithm to ...

In this paper, we introduce a novel method, Multi − Class Sparse Bayesian Regression(MCBR), that generalizes classical approaches such as Ridge regression and ...

We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies ...

MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization.

Jul 22, 2010 · Abstract. The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information.

A novel method, Multi-Class Sparse Bayesian Regression (MCBR), that generalizes classical approaches such as Ridge regression and Automatic Relevance ...

We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies ...

We introduce in this paper a new model, called Multiclass Sparse Bayesian Regression (MCBR), that, unlike classical alternatives, automatically adapts the ...

Oct 22, 2024 · MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient ...