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Regularized Least Square Regression with Unbounded and Dependent Sampling

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  • Xiaorong Chu
  • Hongwei Sun

Abstract

This paper mainly focuses on the least square regression problem for the -mixing and -mixing processes. The standard bound assumption for output data is abandoned and the learning algorithm is implemented with samples drawn from dependent sampling process with a more general output data condition. Capacity independent error bounds and learning rates are deduced by means of the integral operator technique.

Suggested Citation

  • Xiaorong Chu & Hongwei Sun, 2013. "Regularized Least Square Regression with Unbounded and Dependent Sampling," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-7, April.
    • Handle: RePEc:hin:jnlaaa:139318
    DOI: 10.1155/2013/139318
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