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In many pattern recognition/classification problem the true class conditional model and class probabilities are approximated for reasons of reducing complexity and/or of statistical estimation. The approximated classifier is expected to have worse performance, here measured by the probability of correct classification. We present an analysis valid in… (More)

We introduce a novel Markov chain Monte Carlo algorithm for estimation of posterior probabilities over discrete model spaces. Our learning approach is applicable to families of models for which the marginal likelihood can be analytically calculated, either exactly or approximately, given any fixed structure. It is argued that for certain model neighborhood… (More)

- Petra Perner, Anil K. Jain, Timo Koski, Francesco Tortorella, Dragoljub Pokrajac, Tao Wang +20 others
- 2007

- Linus Göransson, Timo Koski
- 2002

In this paper, we evaluate a method for analyzing microarray data. The method is an attempt to learn regulatory interactions between genes from gene expression data. It is based on a Bayesian network, which is a mathematical tool for modeling conditional independences between stochastic variables. We review the dynamic nature of interacting genes, and… (More)

In present paper we study the use of the expectation maximization (EM) algorithm in classiication. The EM-algorithm is used to calculate the probability of each vector belonging to each class. If we assign each vector to the class of maximal probability we get a classiication minimizing a certain log-likelihood function. By analyzing these probabilities we… (More)