Neural Comput - Refined rademacher chaos complexity bounds with applications to the multikernel learning problem.

Tópicos

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Resumo

Estimating the Rademacher chaos complexity of order two is important for understanding the performance of multikernel learning (MKL) machines. In this letter, we develop a novel entropy integral for Rademacher chaos complexities. As compared to the previous bounds, our result is much improved in that it introduces an adjustable parameter e to prohibit the divergence of the involved integral. With the use of the iteration technique in Steinwart and Scovel (2007), we also apply our Rademacher chaos complexity bound to the MKL problems and improve existing learning rates.

Resumo Limpo

estim rademach chao complex order two import understand perform multikernel learn mkl machin letter develop novel entropi integr rademach chao complex compar previous bound result much improv introduc adjust paramet e prohibit diverg involv integr use iter techniqu steinwart scovel also appli rademach chao complex bound mkl problem improv exist learn rate

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