Condition: NEW.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 33.14
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. In English.
Published by Morgan and Claypool Publishers, 2009
ISBN 10: 1598295470 ISBN 13: 9781598295474
Language: English
Seller: Greener Books, London, United Kingdom
Paperback. Condition: Used; Very Good. stains on pages and the side **SHIPPED FROM UK** We believe you will be completely satisfied with our quick and reliable service. All orders are dispatched as swiftly as possible! Buy with confidence! Greener Books.
Published by Springer International Publishing, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 30.53
Convert currencyQuantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines/ Human Semi-Supervised Learning / Theory and Outlook.
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
£ 33.93
Convert currencyQuantity: 5 available
Add to basketPaperback or Softback. Condition: New. Introduction to Semi-Supervised Learning 0.53. Book.
Seller: Books Puddle, New York, NY, U.S.A.
£ 42.28
Convert currencyQuantity: 4 available
Add to basketCondition: New. 1st edition NO-PA16APR2015-KAP.
Published by Springer International Publishing Jun 2009, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
£ 30.53
Convert currencyQuantity: 2 available
Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook 132 pp. Englisch.
Seller: Majestic Books, Hounslow, United Kingdom
Condition: New. Print on Demand.
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Language: English
Seller: moluna, Greven, Germany
£ 29.13
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradi.
Seller: Biblios, Frankfurt am main, HESSE, Germany
£ 45.25
Convert currencyQuantity: 4 available
Add to basketCondition: New. PRINT ON DEMAND.