Titlebook: Machine Learning; Discriminative and G Tony Jebara Book 2004 Springer Science+Business Media New York 2004 Extension.computer science.learn

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Generative Versus Discriminative Learning,and the criteria they optimize. A natural intermediate between the two is conditional learning which helps us visualize the coarse continuum between these two extremes. Figure 2.1 depicts the panorama of approaches as we go horizontally from the generative criteria to discriminative criteria. Simila

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Extensions to MED,pter we explore extensions of this framework spanning a wide variety of learning scenarios. One resounding theme is the introduction of further (possibly intermediate) variables in the discriminant function ., and solving for an augmented distribution .(Θ, …) involving these new terms (Figure 4.1).

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Latent Discrimination,the given task through large margins just as a support vector machine optimizes the margin of linear decision boundaries in Hilbert space. The MED framework is straightforward to solve for exponential family distributions and, in the special case of Gaussian means, subsumes the support vector machin

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Tony Jebaraellen. Mit Hilfe der mechanischen Energie des Antriebmotors werden zunächst Gleich- oder minder periodische Wechselströme erzeugt, die weiter durch besondere Verfahren in hochfrequente umgewandelt werden. Diese dienen dann dazu, elektromagnetische Wellen zu entwickeln, die sich von der Gebestation f

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https://doi.org/10.1007/978-1-4419-9011-2Extension; computer science; learning; machine learning