Abstract
In this paper we introduce a general method that allows to prove tight linear inequalities between different types of predictive complexity and thus we generalise our previous results. The method relies upon probabilistic considerations and allows to describe (using geometrical terms) the sets of coefficients which correspond to true inequalities. We also apply this method to the square-loss and logarithmic complexity and describe their relations which were not covered by our previous research.
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. (1999). General Linear Relations among Different Types of Predictive Complexity. In: Watanabe, O., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1999. Lecture Notes in Computer Science(), vol 1720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46769-6_27
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DOI: https://doi.org/10.1007/3-540-46769-6_27
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