Probability Machines

Probability Machines

J. D. Malley1; J. Kruppa2; A. Dasgupta3; K. G. Malley4; A. Ziegler2 1Center for Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, USA; 2Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Germany; 3Clinical Sciences Section, National Institute of Arthritis and Muscul...

Probability Fuzzy Support Vector Machines

In this paper, a model of probability fuzzy support vector machines (PFSVMs) based on the consideration both for fuzzy clustering and probability distributions is proposed. In many applications of traditional support vector machines (SVMs), there are over-fitting problems due to the fact that SVM is sensitive to outliers or noises. In order to solve the problem, the fuzzy support vector machine...

Probability ANALYSIS OF CASINO SHELF SHUFFLING MACHINES

Evaluation of Support Vector Machines and Minimax Probability Machines for Weather Prediction

The paper evaluates two kernel-based methods on the problem of predicting precipitation based on observable variables. The support vector machine (SVM) method finds the two parallel hyperplanes that provide maximal separation of two subsets, excepting outliers. The minimax probability machine (MPM) method finds an optimal separating hyperplane that minimizes the probability of misclassification...

Learning probability distributions generated by finite-state machines

We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for...