Machine Learning for Multimedia Content Analysis (inbunden)
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Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
277
Utgivningsdatum
2007-10-01
Upplaga
2007 ed.
Förlag
Springer-Verlag New York Inc.
Medarbetare
Xu, Wei
Illustratör/Fotograf
20 Tabellen 20 Abb
Illustrationer
10 Tables, black and white; 20 Illustrations, black and white; XVI, 277 p. 20 illus.
Dimensioner
228 x 152 x 19 mm
Vikt
612 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9780387699387
Machine Learning for Multimedia Content Analysis (inbunden)

Machine Learning for Multimedia Content Analysis

Inbunden Engelska, 2007-10-01
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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).
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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)

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Innehållsförteckning

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.