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Direkte EM- und Loglikelihood-Profil-Verfahren in Intervallkartierungsmodellen der PflanzenzuchtEmrich, Katharina. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2002--Dortmund.
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Entwicklung eines Monte-Carlo-Verfahrens zum selbständigen Lernen von Gauß-MischverteilungenLauer, Martin. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2004--Osnabrück.
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Training of Hidden Markov models as an instance of the expectation maximization algorithmMajewsky, Stefan 27 July 2017 (has links) (PDF)
In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data.
Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that the Baum-Welch algorithm is an instance of the generic EM algorithm introduced by [BSV15], from which follows that all statements about the generic EM algorithm also apply to the Baum-Welch algorithm, especially its correctness and convergence properties.
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Training of Hidden Markov models as an instance of the expectation maximization algorithmMajewsky, Stefan 22 August 2017 (has links)
In Natural Language Processing (NLP), speech and text are parsed and generated with language models and parser models, and translated with translation models. Each model contains a set of numerical parameters which are found by applying a suitable training algorithm to a set of training data.
Many such training algorithms are instances of the Expectation-Maximization (EM) algorithm. In [BSV15], a generic EM algorithm for NLP is described. This work presents a particular speech model, the Hidden Markov model, and its standard training algorithm, the Baum-Welch algorithm. It is then shown that the Baum-Welch algorithm is an instance of the generic EM algorithm introduced by [BSV15], from which follows that all statements about the generic EM algorithm also apply to the Baum-Welch algorithm, especially its correctness and convergence properties.:1 Introduction
1.1 N-gram models
1.2 Hidden Markov model
2 Expectation-maximization algorithms
2.1 Preliminaries
2.2 Algorithmic skeleton
2.3 Corpus-based step mapping
2.4 Simple counting step mapping
2.5 Regular tree grammars
2.6 Inside-outside step mapping
2.7 Review
3 The Hidden Markov model
3.1 Forward and backward algorithms
3.2 The Baum-Welch algorithm
3.3 Deriving the Baum-Welch algorithm
3.3.1 Model parameter and countable events
3.3.2 Tree-shaped hidden information
3.3.3 Complete-data corpus
3.3.4 Inside weights
3.3.5 Outside weights
3.3.6 Complete-data corpus (cont.)
3.3.7 Step mapping
3.4 Review
Appendix
A Elided proofs from Chapter 3
A.1 Proof of Lemma 3.8
A.2 Proof of Lemma 3.9
B Formulary for Chapter 3
Bibliography
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