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Abstract. We present a survey of recent results concerning the theoretical and empirical performance of algorithms for learning regularized least-squares classifiers. The behavior ...
Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integratio...
Finding a good pattern which discriminates one set of strings from the other set is a critical task in knowledge discovery. In this paper, we review a series of our works concernin...
The decomposition method is currently one of the major methods for solving the convex quadratic optimization problems being associated with support vector machines. For a special c...
Different formal learning models address different aspects of learning. Below we compare learning via queries—interpreting learning as a one-shot process in which the learner i...
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...
In this paper we introduce a paradigm for learning in the limit of potentially infinite languages from all positive data and negative counterexamples provided in response to the ...
When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of complexity/current loss renders the analys...