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- W98495954 abstract "This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which has been recently introduced in (6). This frame- work enables computational fusion of training and validation levels and allows to train the model together with finding the regularization constants by solving a single linear system at once. In this paper we show that this framework allows to consider a penalized validation criterion that leads to sparse LS-SVMs. The model, regularization constants and sparseness follow from a convex quadratic program in this case." @default.
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- W98495954 date "2004-01-01" @default.
- W98495954 modified "2023-09-30" @default.
- W98495954 title "Sparse LS-SVMs using Additive Regularization with a Penalized Validation Criterion" @default.
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