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Learning with Submodular Functions - A Convex Optimization Perspective (Paperback)
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Learning with Submodular Functions - A Convex Optimization Perspective (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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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 Lovasz extension
of submodular functions provides a useful set of regularization
functions for supervised and unsupervised learning. In Learning
with Submodular Functions, 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 describes how submodular function minimization is
equivalent to solving a wide variety of convex optimization
problems. This allows the derivation of new efficient algorithms
for approximate and exact submodular function minimization with
theoretical guarantees and good practical performance. By listing
many examples of submodular functions, it reviews various
applications to machine learning, such as clustering, experimental
design, sensor placement, graphical model structure learning or
subset selection, as well as a family of structured
sparsity-inducing norms that can be derived and used from
submodular functions. This is an ideal reference for researchers,
scientists, or engineers with an interest in applying submodular
functions to machine learning problems.
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