Titlebook: Markov Models for Pattern Recognition; From Theory to Appli Gernot A. Fink Textbook 2014Latest edition Springer-Verlag London 2014 Handwrit

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-Gram Modelshe formal description of statistical language models is formed by their representation using Markov chains or so-called .-gram models. A statistical .-gram model corresponds to a Markov chain of order .−1. The probability of a certain symbol sequence is decomposed into a product of conditional proba

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Robust Parameter Estimationilable training samples. Consequently, robust parameter estimation is a primary problem when applying HMMs in practice..In this chapter we will first consider analytical methods which allow to optimize a given feature representation such that the model built on top of it requires less parameters. Th

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Efficient Model Evaluationn order to achieve the efficiency necessary in practical applications, these methods have to be extended and modified such that as many “unnecessary” computations as possible are avoided. This can be achieved by a suitable reorganization of data structures involved or by explicitly discarding “less

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Model Adaptationhe segmentation of . data. This is by definition not part of the training samples and can never be in practical applications. Thus, the characteristic properties of this test data can be predicted to a limited extent only on the basis of the training material. Therefore, in general differences betwe