Acta mathematica scientia,Series A ›› 2014, Vol. 34 ›› Issue (5): 1049-1060.

• Articles •     Next Articles

Generalization Bounds of Compressed Regression Learning Algorithm

 ZHANG Yong-Quan1,2, LI You-Mei1, CAO Fei-Long1, XU Zong-Ben2   

  1. 1.Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou 310018;
    2.Institute for Information and System Sciences, Xi'an Jiaotong University, Xi'an 710049
  • Received:2012-07-28 Revised:2013-12-27 Online:2014-10-25 Published:2014-10-25
  • Supported by:

    国家自然科学基金(11301494)和浙江省自然科学基金(Q12A01026)资助.

Abstract:

This article studies generalization performance of regularized learning algorithm with a general convex loss function and varying Gaussian kernels. Our goal is to give a satisfactory estimate of  generalization error for the learning algorithm. The
generalization error is measured by regularization error and sample error. The regularization error is estimated by constructing a radial basis function (briefly denoted by RBF) neural network in view of the special structures of Gaussian kernels.  The sample error is obtained by using projection operator and covering number of reproducing kernel Hilbert spaces with Gaussian kernels. The obtained results demonstrate the  learning algorithm has good  generalization performance  with suitable choice of m and λ.

Key words: Learning theory, RBF neural network, Gaussian kernels, Generalization error

CLC Number: 

  • 41A25
Trendmd