
Online Learning and Online Convex Optimization
Shai Shalev-Shwartz(Author)
now publishers Inc
1st Edition
Published on 29. March 2012
Book
Paperback/Softback
102 pages
978-1-60198-546-0 (ISBN)
Description
Online learning is a well established learning paradigm which has both theoretical and practical appeals. The goal of online learning is to make a sequence of accurate predictions given knowledge of the correct answer to previous prediction tasks and possibly additional available information. Online learning has been studied in several research fields including game theory, information theory, and machine learning. It also became of great interest to practitioners due the recent emergence of large scale applications such as online advertisement placement and online web ranking. Online Learning and Online Convex Optimization is a modern overview of online learning. Its aim is to provide the reader with a sense of some of the interesting ideas and in particular to underscore the centrality of convexity in deriving efficient online learning algorithms. It connects and relates new results on online convex optimization to classic results on online classification, thus providing a fresh modern perspective on some classic algorithms. It is not intended to be comprehensive but rather to give a high-level, rigorous, yet easy to follow survey of the topic.
More details
Series
Language
English
Place of publication
Hanover
United States
Target group
Professional and scholarly
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 5 mm
Weight
156 gr
ISBN-13
978-1-60198-546-0 (9781601985460)
DOI
10.1561/2200000018
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Schweitzer Classification
Content
1: Introduction 2: Online Convex Optimization 3: Online Classification 4: Limited Feedback (Bandits) 5: Online-to-Batch Conversions. Acknowledgements. References