Artículos relacionados a Introduction to Semi-Supervised Learning (Synthesis...
Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning) - Tapa blanda
Sinopsis
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
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Xiaojin Zhu is an assistant professor in the Computer Sciences department at the University of Wisconsin-Madison. His research interests include statistical machine learning and its applications in cognitive psychology, natural language processing, and programming languages. Xiaojin received his Ph.D. from the Language Technologies Institute at Carnegie Mellon University in 2005. He worked on Mandarin speech recognition as a research staff member at IBM China Research Laboratory in 1996-1998. He received M.S. and B.S. in computer science from Shanghai Jiao Tong University in 1996 and 1993, respectively. His other interests include astronomy and geology. Andrew B.Goldberg is a Ph.D. candidate in the Computer Sciences department at the University of Wisconsin-Madison. His research interests lie in statistical machine learning (in particular, semi-supervised learning) and natural language processing. He has served on the program committee for national and international conferences including AAAI, ACL, EMNLP, and NAACL-HLT. Andrew was the recipient of a UW-Madison First-Year Graduate School Fellowship for 2005-2006 and a Yahoo! Key Technical Challenges Grant for 2008-2009. Before his graduate studies, Andrew received a B.A. in computer science from Amherst College, where he graduated magna cum laude with departmental distinction in 2003. He then spent two years writing, editing, and developing teaching materials for introductory computer science and Web programming textbooks at Deitel and Associates. During this time, he contributed to several Deitel books and co-authored the 3rd edition of Internet & World Wide Web How to Program. In 2005, Andrew entered graduate school at UW-Madison and, in 2006 received his M.S. in computer science. In his free time, Andrew enjoys live music, cooking, photography, and travel.
"Sobre este título" puede pertenecer a otra edición de este libro.
EUR 37,32
EUR 17,08 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envíoComprar nuevo
Ver este artículo
EUR 43,72
EUR 0,64 gastos de envío desde Estados Unidos de America a España
Destinos, gastos y plazos de envío
Resultados de la búsqueda para Introduction to Semi-Supervised Learning (Synthesis...
Imagen de archivo
Introduction to Semi-Supervised Learning
Publicado por
Springer International Publishing AG, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
PAP
Librería: PBShop.store US, Wood Dale, IL, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9783031004209
Cantidad disponible: 1 disponibles
Imagen de archivo
Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. In English. Nº de ref. del artículo: ria9783031004209_new
Cantidad disponible: Más de 20 disponibles
Imagen de archivo
Introduction to Semi-Supervised Learning
Publicado por
Springer International Publishing AG, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
PAP
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: GB-9783031004209
Cantidad disponible: 1 disponibles
Imagen del vendedor
Introduction to Semi-Supervised Learning
Publicado por
Springer International Publishing AG, CH, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
Paperback
Original o primera edición
Librería: Rarewaves.com UK, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 1st. 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. Nº de ref. del artículo: LU-9783031004209
Cantidad disponible: 1 disponibles
Imagen del vendedor
Introduction to Semi-Supervised Learning
Publicado por
Springer International Publishing Jun 2009, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783031004209
Cantidad disponible: 2 disponibles
Imagen de archivo
INTRODUCTION TO SEMI-SUPERVISED LEARNING
Librería: Speedyhen, London, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: NEW. Nº de ref. del artículo: NW9783031004209
Cantidad disponible: 1 disponibles
Imagen del vendedor
Introduction to Semi-Supervised Learning
Publicado por
Springer International Publishing, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783031004209
Cantidad disponible: 1 disponibles
Imagen del vendedor
Introduction to Semi-Supervised Learning
Publicado por
Springer International Publishing AG, CH, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
Paperback
Original o primera edición
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 1st. 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. Nº de ref. del artículo: LU-9783031004209
Cantidad disponible: 1 disponibles
Imagen del vendedor
Introduction to Semi-Supervised Learning (Paperback or Softback)
Publicado por
Springer 6/8/2009, 2009
ISBN 10: 3031004205
ISBN 13: 9783031004209
Nuevo
Paperback or Softback
Librería: BargainBookStores, Grand Rapids, MI, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback or Softback. Condición: New. Introduction to Semi-Supervised Learning 0.53. Book. Nº de ref. del artículo: BBS-9783031004209
Cantidad disponible: 5 disponibles
Imagen de archivo
Introduction to Semi-Supervised Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Librería: Chiron Media, Wallingford, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Paperback. Condición: New. Nº de ref. del artículo: 6666-GRD-9783031004209
Cantidad disponible: 1 disponibles