Regularized Least Square Regression with Unbounded and Dependent Sampling | Zendy

Xiaorong Chu | Zendy; Hongwei Sun | Zendy

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Regularized Least Square Regression with Unbounded and Dependent Sampling

Author(s) -

Xiaorong Chu,

Hongwei Sun

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/139318

Subject(s) - square (algebra) , mathematics , combinatorics , scalable vector graphics , matrix (chemical analysis) , path (computing) , statistics , geometry , computer science , chemistry , chromatography , programming language , operating system

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

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