This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher.
This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher
In this paper we develop a novel probabilistic gen- eralization bound for learning the kernel problem. First, we show that the generalization analysis of.
In this paper we develop a novel probabilistic gen- eralization bound for learning the kernel problem. First, we show that the generalization analysis of.
This paper presents several novel generalization bounds for the problem of learning kernels based on a combinatorial analysis of the Rademacher complexity ...
Dec 17, 2009 · This paper presents several novel generalization bounds for the problem of learning kernels based on the analysis of the Rademacher complexity.
In this paper we develop a novel probabilistic generalization bound for learning the kernel problem. First, we show that the generalization analysis of the ...
By minimizing the derived PEP-based sharp generalization bounds, we de- sign two new kernel learning algorithms for finite kernel and infinite kernels ...
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In this work we adopt the spirit of Rademacher complexity bounds for ERM and SVM with a single kernel [2] to develop an appealing generalization bound for ...
In this paper we develop a novel probabilistic generalization bound for learning the kernel problem. First, we show that the generalization analysis of the ...