Gør som tusindvis af andre bogelskere
Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.
Ved tilmelding accepterer du vores persondatapolitik.Du kan altid afmelde dig igen.
"The aim of this book is to provide the simplest formulations that can be derived "from first principles" with simple arguments"--
Provides the reader with a self-contained view of sparse modeling for visual recognition and image processing. More specifically, the work focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
Presents the theory of submodular functions 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 variety of convex optimization problems.
Presents optimization tools and techniques dedicated to sparsity-inducing penalties from a general perspective. The book covers proximal methods, block-coordinate descent, working-set and homotopy methods, and non-convex formulations and extensions, and provides a set of experiments to compare algorithms from a computational point of view.
Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.
Ved tilmelding accepterer du vores persondatapolitik.