[PDF.19qa] Learning with Submodular Functions: A Convex Optimization Perspective (Foundations and Trends(r) in Machine Learning)

Learning with Submodular Functions: A Convex Optimization Perspective (Foundations and Trends(r) in Machine Learning)

[PDF.is05] Learning with Submodular Functions: A Convex Optimization Perspective (Foundations and Trends(r) in Machine Learning)

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 | #3204457 in Books |  2013-11-21 | Original language:English | PDF # 1 |  9.21 x.54 x6.14l,.81 | File type: PDF | 258 pages

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Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions and (2) the Lovász extension of submodular functions provides a useful set of regularization functions for supervised and unsupervised learning. In Learning with Submodular Functions: A Convex Optimization Perspective, the theory of submodular functions is presented in a self-contained way from a c...

You can specify the type of files you want, for your device.Learning with Submodular Functions: A Convex Optimization Perspective (Foundations and Trends(r) in Machine Learning)   |  Francis Bach. I have read it a couple of times and even shared with my family members. Really good. Couldnt put it down.

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