Supplementary Material: Derivations and Pseudocode for Learning Maximum Margin Hidden Markov Models for Sequence Classification

Nikolaus Mutsam; Franz Pernkopf

Supplementary Material: Derivations and Pseudocode for Learning Maximum Margin Hidden Markov Models for Sequence Classification

Institute of Signal Processing and Speech Communication (4420)

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Abstract

This supplement includes all derivations and the pseudocode for learning the maximum margin hidden Markov model for sequence classification. It uses the extended Baum-Welch framework for optimization.

Original language	English
Journal	Pattern Recognition Letters
Volume	77
Publication status	Published - 2016

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title = "Supplementary Material: Derivations and Pseudocode for Learning Maximum Margin Hidden Markov Models for Sequence Classification",

abstract = "This supplement includes all derivations and the pseudocode for learning the maximum margin hidden Markov model for sequence classification. It uses the extended Baum-Welch framework for optimization.",

author = "Nikolaus Mutsam and Franz Pernkopf",

year = "2016",

language = "English",

volume = "77",

journal = "Pattern Recognition Letters ",

issn = "0167-8655",

publisher = "Elsevier B.V.",

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AU - Mutsam, Nikolaus

AU - Pernkopf, Franz

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SN - 0167-8655

VL - 77

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

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Supplementary Material: Derivations and Pseudocode for Learning Maximum Margin Hidden Markov Models for Sequence Classification

Abstract

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