• 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.

From the reviews: "The objectives of this book are to bring together powerful machine learning techniques that are suitable for modeling multimedia data, and to showcase their application to common multimedia content analysis tasks. The book is designed for students and researchers who want to apply machine learning techniques to multimedia content analysis. ... Motivated researchers working in this field can certainly benefit by reading about the methods and case studies described here. It could also serve as a good reference ... ." (Rao Vemuri, Computing Reviews, Vol. 50 (1), January, 2009)

• 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.

From the reviews: "The objectives of this book are to bring together powerful machine learning techniques that are suitable for modeling multimedia data, and to showcase their application to common multimedia content analysis tasks. The book is designed for students and researchers who want to apply machine learning techniques to multimedia content analysis. ... Motivated researchers working in this field can certainly benefit by reading about the methods and case studies described here. It could also serve as a good reference ... ." (Rao Vemuri, Computing Reviews, Vol. 50 (1), January, 2009)

• 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.

From the reviews: "The objectives of this book are to bring together powerful machine learning techniques that are suitable for modeling multimedia data, and to showcase their application to common multimedia content analysis tasks. The book is designed for students and researchers who want to apply machine learning techniques to multimedia content analysis. ... Motivated researchers working in this field can certainly benefit by reading about the methods and case studies described here. It could also serve as a good reference ... ." (Rao Vemuri, Computing Reviews, Vol. 50 (1), January, 2009)