Machine learning for multimedia content analysis
著者
書誌事項
Machine learning for multimedia content analysis
（Multimedia systems and applications series, v. 30）
Springer, c2007
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注記
Bibliography: p. [269]-274
Includes index
内容説明・目次
内容説明
This volume introduces machine learning techniques that are particularly powerful and effective for modeling multimedia data and common tasks of multimedia content analysis. It systematically covers key machine learning techniques in an intuitive fashion and demonstrates their applications through case studies. Coverage includes examples of unsupervised learning, generative models and discriminative models. In addition, the book examines Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM).
目次
Unsupervised Learning.- Dimension Reduction.- Data Clustering Techniques.- Generative Graphical Models.- of Graphical Models.- Markov Chains and Monte Carlo Simulation.- Markov Random Fields and Gibbs Sampling.- Hidden Markov Models.- Inference and Learning for General Graphical Models.- Discriminative Graphical Models.- Maximum Entropy Model and Conditional Random Field.- Max-Margin Classifications.
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