optimization for Machine learning
(Paperback)
PHI Learning (Publisher)
Published
2015
Reviews
1
Ships within 2-4 days
Out Of Stock
₹283.00
₹399.00
29% off(Paperback)
PHI Learning (Publisher)
Ships within 2-4 days
Out Of Stock
The interplay between optimization and machine learning is one of the most important developments in modern computational science.
Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today’s machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
About the AuthorSUVRIT SRA is a Research Scientist at the Max Planck Institute for Biological Cybernetics, Tübingen, Germany
|SEBASTIAN NOWOZIN is a Postdoctoral Researcher at Microsoft Research, Cambridge, UK.
|STEPHEN J. WRIGHT is Professor in the Computer Sciences Department at the University of Wisconsin, Madison.
