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Online Learning and Online Convex Optimization: 12 (Foundations and Trends® in Machine Learning) - Tapa blanda
Sinopsis
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.
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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.
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Online Learning and Online Convex Optimization (Foundations and Trends(r) in Machine Learning)
Librería: California Books, Miami, FL, Estados Unidos de America
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Online Learning and Online Convex Optimization
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Online Learning and Online Convex Optimization
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Condición: New. Nº de ref. del artículo: 16615742-n
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Online Learning and Online Convex Optimization
Publicado por
now publishers Inc, US, 2012
ISBN 10: 1601985460
ISBN 13: 9781601985460
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Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 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. Nº de ref. del artículo: LU-9781601985460
Cantidad disponible: Más de 20 disponibles
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Online Learning and Online Convex Optimization
Publicado por
now publishers Inc, US, 2012
ISBN 10: 1601985460
ISBN 13: 9781601985460
Nuevo
Paperback
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 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. Nº de ref. del artículo: LU-9781601985460
Cantidad disponible: Más de 20 disponibles
Imagen del vendedor
Online Learning and Online Convex Optimization
Publicado por
now publishers Inc, US, 2012
ISBN 10: 1601985460
ISBN 13: 9781601985460
Nuevo
Paperback
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 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. Nº de ref. del artículo: LU-9781601985460
Cantidad disponible: Más de 20 disponibles
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Online Learning and Online Convex Optimization
Librería: moluna, Greven, Alemania
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Condición: New. Inhaltsverzeichnis1: Introduction 2: Online Convex Optimization 3: Online Classification 4: Limited Feedback (Bandits) 5: Online-to-Batch Conversions. Acknowledgements. ReferencesKlappentextrnrnOnline learning is a we. Nº de ref. del artículo: 329708486
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Online Learning and Online Convex Optimization
Publicado por
now publishers Inc, 2012
ISBN 10: 1601985460
ISBN 13: 9781601985460
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Online Learning and Online Convex Optimization
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Condición: New. Nº de ref. del artículo: 16615742-n
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Online Learning and Online Convex Optimization
Publicado por
Now Publishers, 2012
ISBN 10: 1601985460
ISBN 13: 9781601985460
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Condición: As New. Unread book in perfect condition. Nº de ref. del artículo: 16615742
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