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.
- ISBN13 9781601985460
- Publish Date 29 March 2012
- Publish Status Active
- Publish Country US
- Imprint now publishers Inc
- Format Paperback
- Pages 102
- Language English
- URL https://nowpublishers.com/article/Details/MAL-018