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What is included with this book?
Introduction | p. 1 |
Basic Statistical Learning Problems | p. 2 |
Categorizations of Machine Learning Techniques | p. 4 |
Unsupervised vs. Supervised | p. 4 |
Generative Models vs. Discriminative Models | p. 4 |
Models for Simple Data vs. Models for Complex Data | p. 6 |
Model Identification vs. Model Prediction | p. 7 |
Multimedia Content Analysis | p. 8 |
Unsupervised Learning | |
Dimension Reduction | p. 15 |
Objectives | p. 15 |
Singular Value Decomposition | p. 16 |
Independent Component Analysis | p. 20 |
Preprocessing | p. 23 |
Why Gaussian is Forbidden | p. 24 |
Dimension Reduction by Locally Linear Embedding | p. 26 |
Case Study | p. 30 |
Problems | p. 34 |
Data Clustering Techniques | p. 37 |
Introduction | p. 37 |
Spectral Clustering | p. 39 |
Problem Formulation and Criterion Functions | p. 39 |
Solution Computation | p. 42 |
Example | p. 46 |
Discussions | p. 50 |
Data Clustering by Non-Negative Matrix Factorization | p. 51 |
Single Linear NMF Model | p. 52 |
Bilinear NMF Model | p. 55 |
Spectral vs. NMF | p. 59 |
Case Study: Document Clustering Using Spectral and NMF Clustering Techniques | p. 61 |
Document Clustering Basics | p. 62 |
Document Corpora | p. 64 |
Evaluation Metrics | p. 64 |
Performance Evaluations and Comparisons | p. 65 |
Generative Graphical Models | |
Introduction of Graphical Models | p. 73 |
Directed Graphical Model | p. 74 |
Undirected Graphical Model | p. 77 |
Generative vs. Discriminative | p. 79 |
Content of Part II | p. 80 |
Markov Chains and Monte Carlo Simulation | p. 81 |
Discrete-Time Markov Chain | p. 81 |
Canonical Representation | p. 84 |
Definitions and Terminologies | p. 88 |
Stationary Distribution | p. 91 |
Long Run Behavior and Convergence Rate | p. 94 |
Markov Chain Monte Carlo Simulation | p. 100 |
Objectives and Applications | p. 100 |
Rejection Sampling | p. 101 |
Markov Chain Monte Carlo | p. 104 |
Rejection Sampling vs. MCMC | p. 110 |
Problems | p. 112 |
Markov Random Fields and Gibbs Sampling | p. 115 |
Markov Random Fields | p. 115 |
Gibbs Distributions | p. 117 |
Gibbs - Markov Equivalence | p. 120 |
Gibbs Sampling | p. 123 |
Simulated Annealing | p. 126 |
Case Study: Video Foreground Object Segmentation by MRF | p. 133 |
Objective | p. 134 |
Related Works | p. 134 |
Method Outline | p. 135 |
Overview of Sparse Motion Layer Computation | p. 136 |
Dense Motion Layer Computation Using MRF | p. 138 |
Bayesian Inference | p. 140 |
Solution Computation by Gibbs Sampling | p. 141 |
Experimental Results | p. 143 |
Problems | p. 146 |
Hidden Markov Models | p. 149 |
Markov Chains vs. Hidden Markov Models | p. 149 |
Three Basic Problems for HMMs | p. 153 |
Solution to Likelihood Computation | p. 154 |
Solution to Finding Likeliest State Sequence | p. 158 |
Solution to HMM Training | p. 160 |
Expectation-Maximization Algorithm and its Variances | p. 162 |
Expectation-Maximization Algorithm | p. 162 |
Baum-Welch Algorithm | p. 164 |
Case Study: Baseball Highlight Detection Using HMMs | p. 167 |
Objective | p. 167 |
Overview | p. 167 |
Camera Shot Classification | p. 169 |
Feature Extraction | p. 172 |
Highlight Detection | p. 173 |
Experimental Evaluation | p. 174 |
Problems | p. 175 |
Inference and Learning for General Graphical Models | p. 179 |
Introduction | p. 179 |
Sum-product algorithm | p. 182 |
Max-product algorithm | p. 188 |
Approximate inference | p. 189 |
Learning | p. 191 |
Problems | p. 196 |
Discriminative Graphical Models | |
Maximum Entropy Model and Conditional Random Field | p. 201 |
Overview of Maximum Entropy Model | p. 202 |
Maximum Entropy Framework | p. 204 |
Feature Function | p. 204 |
Maximum Entropy Model Construction | p. 205 |
Parameter Computation | p. 208 |
Comparison to Generative Models | p. 210 |
Relation to Conditional Random Field | p. 213 |
Feature Selection | p. 215 |
Case Study: Baseball Highlight Detection Using Maximum Entropy Model | p. 217 |
System Overview | p. 218 |
Highlight Detection Based on Maximum Entropy Model | p. 220 |
Multimedia Feature Extraction | p. 222 |
Multimedia Feature Vector Construction | p. 226 |
Experiments | p. 227 |
Problems | p. 232 |
Max-Margin Classifications | p. 235 |
Support Vector Machines (SVMs) | p. 236 |
Loss Function and Risk | p. 237 |
Structural Risk Minimization | p. 237 |
Support Vector Machines | p. 239 |
Theoretical Justification | p. 243 |
SVM Dual | p. 244 |
Kernel Trick | p. 245 |
SVM Training | p. 248 |
Further Discussions | p. 255 |
Maximum Margin Markov Networks | p. 257 |
Primal and Dual Problems | p. 257 |
Factorizing Dual Problem | p. 259 |
General Graphs and Learning Algorithm | p. 262 |
Max-Margin Networks vs. Other Graphical Models | p. 262 |
Problems | p. 264 |
Appendix | p. 267 |
References | p. 269 |
Index | p. 275 |
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The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.