Complexity Approximation Principle and Rissanen's Approach to Real-Valued Parameters

Authors:
Yuri Kalnishkan
Affiliations:
-
Venue:
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Year:
2000

Citing 5
Cited 0

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Abstract

In this paper an application of the Complexity Approximation Principle to the non-linear regression is suggested. We combine this principle with the approximation of the complexity of a real-valued vector parameter proposed by Rissanen and thus derive a method for the choice of parameters in the non-linear regression.