Titlebook: Machine Learning for Multimedia Content Analysis; Yihong Gong,Wei Xu Book 2007 Springer-Verlag US 2007 DOM.Dimensionsreduktion.Gong.Hidden

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書目名稱Machine Learning for Multimedia Content Analysis影響因子(影響力)




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Psa617

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Peculate

Inference and Learning for General Graphical ModelsIn previous chapters, we described several probabilistic models that capture certain structures of the given data. In this chapter, we will see that these models are all under a general umbrella called probabilistic graphical models.

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Inertia

Machine Learning for Multimedia Content Analysis978-0-387-69942-4Series ISSN 1568-2358 Series E-ISSN 2945-5715

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thrombus

1568-2358 cludes examples of unsupervised learning, generative models .Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, is

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攀登

Markov Chains and Monte Carlo Simulationbution and associated theorems. At the end of this chapter, we present the Markov Chain Monte Carlo simulation (MCMC) that is one of the most important applications of Markov chains for probabilistic data sampling and model estimations.