Semi-Supervised and Unsupervised Machine Learning (Hardcover)

Amparo Albalate

ISBN 10: 1848212038 / ISBN 13: 9781848212039

Editore: ISTE Ltd and John Wiley & Sons Inc, London, 2010

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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.

Codice articolo 9781848212039

Dati bibliografici

• Titolo: Semi-Supervised and Unsupervised Machine Learning (Hardcover)
• Editore: ISTE Ltd and John Wiley & Sons Inc, London
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1848212038
• ISBN 13: 9781848212039
• Rilegatura: Hardcover
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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

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