• جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 921
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     in this paper we study a class of regularized kernel methods for multi-output learning which are based on filtering the spectrum of the kernel matrix. the considered methods include tikhonov regularization as a special case, as well as interesting alternatives such as vector-valued extensions of l2 boosting and other iterative schemes. computational properties are discussed for various examples of kernels for vector-valued functions and the benefits of iterative techniques are illustrated. generalizing previous results for the scalar case, we show a finite sample bound for the excess risk of the obtained estimator, which allows to prove consistency both for regression and multi-category classification. finally, we present some promising results of the proposed algorithms on artificial and real data.

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