COLT '90 Proceedings of the third annual workshop on Computational learning theory
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Journal of the ACM (JACM)
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
A game of prediction with expert advice
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Theoretical Computer Science
On complexity of easy predictable sequences
Information and Computation
Predictive Complexity and Information
COLT '02 Proceedings of the 15th Annual Conference on Computational Learning Theory
| Hi-index | 0.01 |
The notions of predictive complexity and of corresponding amount of information are considered. Predictive complexity is a generalization of Kolmogorov complexity which bounds the ability of any algorithm to predict elements of a sequence of outcomes. We consider predictive complexity for a wide class of bounded loss functions which are generalizations of square-loss function. Relations between unconditional KG(x) and conditional KG(x|y) predictive complexities are studied. We define an algorithm which has some ''expanding property''. It transforms with positive probability sequences of given predictive complexity into sequences of essentially bigger predictive complexity. A concept of amount of predictive information IG(y:x) is studied. We show that this information is noncommutative in a very strong sense and present asymptotic relations between values IG(y:x), IG(x:y), KG(x) and KG(y).