Learning with Submodular Functions
A Convex Optimization Perspective
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 convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems. In particular, it des…
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Produktdetails
- ISBN: 978-1-60198-756-3
- EAN: 9781601987563
- Produktnummer: 19330101
- Verlag: now publishers Inc
- Sprache: Englisch
- Erscheinungsjahr: 2013
- Seitenangabe: 258 S.
- Masse: H23.4 cm x B15.6 cm x D1.4 cm 390 g
- Gewicht: 390
- Sonstiges: General (US: Trade)
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