In this paper, we derive lower and upper bounds for the probability of error for a linear classifier, where the random vectors representing the underlying classes obey the multivar...
This paper presents a new approach to understand the event stream model. Additionally a new approximation algorithm for the feasibility test of the sporadic and the generalized mu...
Abstract. For many statistical pattern recognition methods, distributions of sample vectors are assumed to be normal, and the quadratic discriminant function derived from the proba...
Compilation is an important approach to a range of inference problems, since it enables linear-time inference in the size S of the compiled representation. However, the main drawb...
We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the paramet...