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1.

電子ブック

EB
Thomas Plötz, Gernot A. Fink, Thomas Plötz
出版情報: Springer eBooks Computer Science , Springer London, 2011
所蔵情報: loading…
2.

電子ブック

EB
Gernot A. Fink
出版情報: Springer eBooks Computer Science , Springer Berlin Heidelberg, 2008
所蔵情報: loading…
目次情報: 続きを見る
Introduction / 1:
Thematic Context / 1.1:
Functional Principles of Markov Models / 1.2:
Goal and Structure of the Book / 1.3:
Application Areas / 2:
Speech / 2.1:
Writing / 2.2:
Biological Sequences / 2.3:
Outlook / 2.4:
Theory / Part I:
Foundations of Mathematical Statistics / 3:
Random Experiment, Event, and Probability / 3.1:
Random Variables and Probability Distributions / 3.2:
Parameters of Probability Distributions / 3.3:
Normal Distributions and Mixture Models / 3.4:
Stochastic Processes and Markov Chains / 3.5:
Principles of Parameter Estimation / 3.6:
Maximum Likelihood Estimation / 3.6.1:
Maximum a posteriori Estimation / 3.6.2:
Bibliographical Remarks / 3.7:
Vector Quantization / 4:
Definition / 4.1:
Optimality / 4.2:
Nearest-Neighbor Condition
Centroid Condition
Algorithms for Vector Quantizer Design / 4.3:
Lloyd's Algorithm
LBG Algorithm
k-Means-Algorithm
Estimation of Mixture Density Models / 4.4:
EM algorithm
Hidden Markov Models / 4.5:
Modeling Emissions / 5.1:
Use Cases / 5.3:
Notation / 5.4:
Evaluation / 5.5:
The Production Probability / 5.5.1:
Forward Algorithm
The "Optimal" Production Probability / 5.5.2:
Decoding / 5.6:
Viterbi Algorithm
Parameter Estimation / 5.7:
Foundations / 5.7.1:
Forward-Backward Algorithm
Training Methods / 5.7.2:
Baum-Welch Algorithm
Viterbi training
Segmental k-Means
Multiple Observation Sequences / 5.7.3:
Model Variants / 5.8:
Alternative Algorithms / 5.8.1:
Alternative Model Architectures / 5.8.2:
n-Gram Models / 5.9:
Redistribution of Probability Mass / 6.1:
Discounting
Incorporation of More General Distributions / 6.5.2:
Interpolation
Backing Off
Optimization of Generalized Distributions / 6.5.3:
Category-Based Models / 6.6:
Longer Temporal Dependencies / 6.6.2:
Practice / 6.7:
Computations with Probabilities / 7:
Logarithmic Probability Representation / 7.1:
Lower Bounds for Probabilities / 7.2:
Codebook Evaluation for Semi-Continuous HMMs / 7.3:
Probability Ratios / 7.4:
Configuration of Hidden Markov Models / 8:
Model Topologies / 8.1:
Modularization / 8.2:
Context-Independent Sub-Word Units / 8.2.1:
Context-Dependent Sub-Word Units / 8.2.2:
Compound Models / 8.3:
Profile HMMs / 8.4:
Robust Parameter Estimation / 8.5:
Feature Optimization / 9.1:
Decorrelation / 9.1.1:
Principal Component Analysis I
Whitening
Dimensionality Reduction / 9.1.2:
Principal Component Analysis II
Linear Discriminant Analysis
Tying / 9.2:
Model Subunits / 9.2.1:
State Tying / 9.2.2:
Tying in Mixture Models / 9.2.3:
Initialization of Parameters / 9.3:
Efficient Model Evaluation / 10:
Efficient Evaluation of Mixture Densities / 10.1:
Beam Search / 10.2:
Efficient Parameter Estimation / 10.3:
Forward-Backward Pruning / 10.3.1:
Segmental Baum-Welch Algorithm / 10.3.2:
Training of Model Hierarchies / 10.3.3:
Tree-like Model Organization / 10.4:
HMM Prefix Trees / 10.4.1:
Tree-like Representation for n-Gram Models / 10.4.2:
Model Adaptation / 11:
Basic Principles / 11.1:
Adaptation of Hidden Markov Models / 11.2:
Maximum-Likelihood Linear-Regression
Adaptation of n-Gram Models / 11.3:
Cache Models / 11.3.1:
Dialog-Step Dependent Models / 11.3.2:
Topic-Based Language Models / 11.3.3:
Integrated Search Methods / 12:
HMM Networks / 12.1:
Multi-Pass Search / 12.2:
Search Space Copies / 12.3:
Context-Based Search Space Copies / 12.3.1:
Time-Based Search Space Copies / 12.3.2:
Language-Model Look-Ahead / 12.3.3:
Time-Synchronous Parallel Model Decoding / 12.4:
Generation of Segment Hypotheses / 12.4.1:
Language Model-Based Search / 12.4.2:
Systems / Part III:
Speech Recognition / 13:
Recognition System of RWTH Aachen University / 13.1:
BBN Speech Recognizer BYBLOS / 13.2:
ESMERALDA / 13.3:
Character and Handwriting Recognition / 14:
OCR System by BBN / 14.1:
Duisburg Online Handwriting Recognition System / 14.2:
ESMERALDA Offline Recognition System / 14.3:
Analysis of Biological Sequences / 15:
HMMER / 15.1:
SAM / 15.2:
References / 15.3:
Index
Introduction / 1:
Thematic Context / 1.1:
Functional Principles of Markov Models / 1.2:
3.

電子ブック

EB
Gernot A. Fink
出版情報: Springer eBooks Computer Science , Springer London, 2014
所蔵情報: loading…
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