Semi-Supervised and Unsupervised Machine Learning (Hardcover)

Amparo Albalate

ISBN 10: 1848212038 / ISBN 13: 9781848212039

Published by ISTE Ltd and John Wiley & Sons Inc, London, 2010

About this Item

Hardcover. This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described. * Provides a detailed and up-to-date overview on classification and data mining methods. * Focuses on supervised classification algorithms and their applications, including recent research on the combination of classifiers. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781848212039

Bibliographic Details

• Title: Semi-Supervised and Unsupervised Machine Learning (Hardcover)
• Publisher: ISTE Ltd and John Wiley & Sons Inc, London
• Publication Date: 2010
• Language: English
• ISBN 10: 1848212038
• ISBN 13: 9781848212039
• Binding: Hardcover
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

This book provides a detailed and up-to-date overview on classification and data mining methods. The first part is focused on supervised classification algorithms and their applications, including recent research on the combination of classifiers. The second part deals with unsupervised data mining and knowledge discovery, with special attention to text mining. Discovering the underlying structure on a data set has been a key research topic associated to unsupervised techniques with multiple applications and challenges, from web-content mining to the inference of cancer subtypes in genomic microarray data. Among those, the book focuses on a new application for dialog systems which can be thereby made adaptable and portable to different domains. Clustering evaluation metrics and new approaches, such as the ensembles of clustering algorithms, are also described.

About the Author

Amparo Albalate,?University of Ulm, Institute of Information Technology, Germany.

Wolfgang Minker, University of Ulm, Institute of Information Technology, Germany.