Final Program and Proceedings of IS & T's NIP 18
Author: IS & T--the Society for Imaging Science and Technology
Publisher:
Published: 2002
Total Pages: 845
ISBN-13: 9780892082407
DOWNLOAD EBOOK →Author: IS & T--the Society for Imaging Science and Technology
Publisher:
Published: 2002
Total Pages: 845
ISBN-13: 9780892082407
DOWNLOAD EBOOK →Author: William R. Wiener
Publisher: American Foundation for the Blind
Published: 2010
Total Pages: 856
ISBN-13: 0891284613
DOWNLOAD EBOOK →Foundations of Orientation and Mobility, the classic professional reference and textbook has been completely revised and expanded to two volumes by the most knowledgeable experts in the field. The new third edition includes both the latest research in O&M and expanded information on practice and teaching strategies. Volume 2, Instructional Strategies and Practical Applications, contains detailed information in such as areas as the use of the senses in O&M; teaching O&M to different age and ability groups; the use of technology-based travel systems; and travel in complex environments. No O&M student or professional can afford to be without this essential resource.
Author: American Public Health Association
Publisher:
Published: 1981
Total Pages: 1254
ISBN-13:
DOWNLOAD EBOOK →Author: Canadian Pulp and Paper Association. Technical Section
Publisher:
Published: 1992
Total Pages: 794
ISBN-13:
DOWNLOAD EBOOK →Annual meeting held after the end of the calendar year covered by the proceedings.
Author: Suvrit Sra
Publisher: MIT Press
Published: 2012
Total Pages: 509
ISBN-13: 026201646X
DOWNLOAD EBOOK →An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. 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.