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Introduction to Semi-Supervised Learning
Language: English
Published by Springer International Publishing AG, CH, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
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First Edition
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Introduction to Semi-Supervised Learning
Language: English
Published by Springer International Publishing AG, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
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Introduction to Semi-Supervised Learning
Language: English
Published by Springer International Publishing AG, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
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Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
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Introduction to Semi-supervised Learning
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Introduction to Semi-supervised Learning
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Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Language: English
Published by Morgan and Claypool Publishers, 2009
ISBN 10: 1598295470 ISBN 13: 9781598295474
Paperback. Condition: Used; Good. ***Simply Brit*** Welcome to our online used book store, where affordability meets great quality. Dive into a world of captivating reads without breaking the bank. We take pride in offering a wide selection of used books, from classics to hidden gems, ensuring there is something for every literary palate. All orders are shipped within 24 hours and our lightning fast-delivery within 48 hours coupled with our prompt customer service ensures a smooth journey from ordering to delivery. Discover the joy of reading with us, your trusted source for affordable books that do not compromise on quality.
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Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning, 6)
Language: English
Published by Morgan and Claypool Publishers, 2009
ISBN 10: 1598295470 ISBN 13: 9781598295474
Condition: New. New.
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Introduction to Semi-Supervised Learning
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Condition: New. 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.
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Taschenbuch. 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.
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Taschenbuch. Condition: Neu. Introduction to Semi-Supervised Learning | Xiaojin Zhu (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xii | Englisch | 2009 | Springer | EAN 9783031004209 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Introduction to Semi-Supervised Learning
Language: English
Published by Springer International Publishing AG, CH, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
First Edition
Paperback. Condition: New. 1st.
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Introduction to Semi-Supervised Learning
Language: English
Published by Springer International Publishing Jun 2009, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. 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.
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Introduction to Semi-Supervised Learning
Language: English
Published by Springer, Springer Jun 2009, 2009
ISBN 10: 3031004205 ISBN 13: 9783031004209
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 OutlookSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 132 pp. Englisch.
