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.
Supported partially by EPSRC through the grant GR/M14937 (“Predictive complexity: recursion-theoretic variants”) and by ORS Awards Scheme.
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Kalnishkan, Y. (2000). Complexity Approximation Principle and Rissanen’s Approach to Real-Valued Parameters. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_21
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DOI: https://doi.org/10.1007/3-540-45164-1_21
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