"Similar problems have similar solutions" is a basic tenet of case-based inference. However this is not satisfied for CBR systems where the task is to achieve original so...
Time-series segmentation in the fully unsupervised scenario in which the number of segment-types is a priori unknown is a fundamental problem in many applications. We propose a Ba...
This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Model...
In this paper, we introduce a method for approximating the solution to inference and optimization tasks in uncertain and deterministic reasoning. Such tasks are in general intract...
In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discreteoutcomes,most of which do not appearin the training ...