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Produktbild: Markov Models for Pattern Recognition
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Markov Models for Pattern Recognition From Theory to Applications

Aus der Reihe Advances in Computer Vision and Pattern Recognition
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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.10.2010

Abbildungen

51 schwarzweisse Abbildungen,

Verlag

Springer Berlin

Seitenzahl

248

Maße (L/B/H)

24,4/15,9/2 cm

Gewicht

403 g

Auflage

Softcover reprint of hardcover 1st ed. 2008

Originaltitel

Mustererkennung mit Markov-Modellen

Sprache

Englisch

ISBN

978-3-642-09088-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

15.10.2010

Abbildungen

51 schwarzweisse Abbildungen,

Verlag

Springer Berlin

Seitenzahl

248

Maße (L/B/H)

24,4/15,9/2 cm

Gewicht

403 g

Auflage

Softcover reprint of hardcover 1st ed. 2008

Originaltitel

Mustererkennung mit Markov-Modellen

Sprache

Englisch

ISBN

978-3-642-09088-2

Herstelleradresse

Springer Heidelberg
Tiergartenstr. 17
69121 Heidelberg
DE
buchhandel-buch@springer.com

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  • Produktbild: Markov Models for Pattern Recognition
  • 1. Introduction
    1.1 Thematic Context
    1.2 Capabilities of Markov Models
    1.3 Goal and Structure
    2. Application Areas
    2.1 Speech
    2.2 Handwriting
    2.3 Biological Sequences
    2.4 Outlook
    Part I: Theory
    3. Foundations of Mathematical Statistics
    3.1 Experiment, Event, and Probability
    3.2 Random Variables and Probability Distributions
    3.3 Parameters of Probability Distributions
    3.4 Normal Distributions and Mixture Density Models
    3.5 Stochastic Processes and Markov Chains
    3.6 Principles of Parameter Estimation
    3.7 Bibliographical Remarks
    4. Vector Quantisation
    4.1 Definition
    4.2 Optimality
    4.3 Algorithms for Vector Quantiser Design (LLoyd, LBG, k-means)
    4.4 Estimation of Mixture Density Models
    4.5 Bibliographical Remarks
    5. Hidden-Markov Models
    5.1 Definition
    5.2 Modeling of Output Distributions
    5.3 Use-Cases
    5.4 Notation
    5.5 Scoring (Forward algorithm)
    5.6 Decoding (Viterbi algorithm)
    5.7 Parameter Estimation (Forward-backward algorithm, Baum-Welch, Viterbi, and segmental k-means training)
    5.8 Model Variants
    5.9 Bibliographical Remarks
    6. n-Gram Models
    6.1 Definition
    6.2 Use-Cases
    6.3 Notation
    6.4 Scoring
    6.5 Parameter Estimation (discounting, interpolation and backing-off)
    6.6 Model Variants (categorial models, long-distance dependencies)
    6.7 Bibliographical Remarks
    Part II: Practical Aspects
    7. Computations with Probabilities
    7.1 Logarithmic Probability Representation
    7.2 Flooring of Probabilities
    7.3 Codebook Evaluation in Tied-Mixture Models
    7.4 Likelihood Ratios
    8. Configuration of Hidden-Markov Models
    8.1 Model Topologies
    8.2 Sub-Model Units
    8.3 Compound Models
    8.4 Profile-HMMs
    8.5 Modelling of Output Probability Densities
    9. Robust Parameter Estimation
    9.1 Optimization of Feature Representations (Principle component analysis, whitening, linear discriminant analysis)
    9.2 Tying (of model parameters, especially: mixture tying)
    9.3 Parameter Initialization
    10. Efficient Model Evaluation
    10.1 Efficient Decoding of Mixture Densities
    10.2 Beam Search
    10.3 Efficient Parameter Estimation (forward-backward pruning, segmental Baum-Welch, training of model hierarchies)
    10.4 Tree-based Model Representations
    11. Model Adaptation
    11.1 Foundations of Adaptation
    11.2 Adaptation of Hidden-Markov Models (Maximum-likelihood linear regression)
    11.3 Adaptation of n-Gram Models (cache models, dialog-step dependent models, topic-based language models)
    12. Integrated Search
    12.1 HMM Networks
    12.2 Multi-pass Search Strategies
    12.3 Search-Space Copies (context and time-based tree copying strategies, language model look-ahead)
    12.4 Time-synchronous Integrated Decoding
    Part III: Putting it All Together
    13. Speech Recognition
    13.1 Application-Specific Processing (feature extraction, vocal tract length normalization, ...)
    13.2 Systems (e.g. BBN Byblos, SPHINX III, ...)
    14. Text Recognition
    14.1 Application-Specific Processing (linearization of data representation for off-line applications, preprocessing, normalization, feature extraction)
    14.2 Systems for On-line Handwriting Recognition
    14.3 Systems for Off-line Handwriting Recognition
    15. Analysis of Biological Sequences
    15.1 Representation of Biological Sequences
    15.2 Systems (HMMer, SAM, Meta-MEME)