Abstract. We give a lower bound for the error of any unitarily invariant algorithm learning half-spaces against the uniform or related distributions on the unit sphere. The bound i...
Abstract. We consider an upper confidence bound algorithm for Markov decision processes (MDPs) with deterministic transitions. For this algorithm we derive upper bounds on the onl...
Abstract. We propose a method of unsupervised learning from stationary, vector-valued processes. A low-dimensional subspace is selected on the basis of a criterion which rewards da...
Abstract. A hidden Markov model is introduced for descriptive modelling the mosaic–like structures of haplotypes, due to iterated recombinations within a population. Methods usin...
Mikko Koivisto, Teemu Kivioja, Heikki Mannila, Pas...
Abstract. The explosion of data stored in commercial or administrational databases calls for intelligent techniques to discover the patterns hidden in them and thus to exploit all ...