399,59 €
443,99 €
-10% with code: EXTRA
Machine Learning for Multimedia Content Analysis
Machine Learning for Multimedia Content Analysis
399,59
443,99 €
  • We will send in 10–14 business days.
Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. 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 l…
443.99
  • Publisher:
  • ISBN-10: 1441943536
  • ISBN-13: 9781441943538
  • Format: 15.6 x 23.4 x 1.6 cm, minkšti viršeliai
  • Language: English
  • SAVE -10% with code: EXTRA

Machine Learning for Multimedia Content Analysis (e-book) (used book) | bookbook.eu

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Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. 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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  • Author: Yihong Gong
  • Publisher:
  • ISBN-10: 1441943536
  • ISBN-13: 9781441943538
  • Format: 15.6 x 23.4 x 1.6 cm, minkšti viršeliai
  • Language: English English

Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. 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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