Abstract
The generalization performance is the important property of learning machines. The desired learning machines should have the quality of stability with respect to the training samples. We consider the empirical risk minimization on the function sets which are eliminated noisy. By applying the Kutin’s inequality we establish the bounds of the rate of uniform convergence of the empirical risks to their expected risks for learning machines and compare the bounds with known results.
Supported in part by NSFC under grant 10371033.
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Zou, B., Li, L., Xu, J. (2005). The Bounds on the Rate of Uniform Convergence for Learning Machine. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_86
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DOI: https://doi.org/10.1007/11427391_86
Publisher Name: Springer, Berlin, Heidelberg
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