9783540789802 - Supervised and Unsupervised Ensemble Methods and Their Applications: 126 (21 risultati)

Hardcover. Condizione: Très bon. Ancien livre de bibliothèque. Salissures sur la tranche. Edition 2008. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Stains on the edge. Edition 2008. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Condizione: New. pp. 196.

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Condizione: New. pp. 196 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

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Condizione: New. pp. 196.

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Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | majority vote.

Hardcover. Condizione: new. Hardcover. The rapidly growing amount of data, available from di?erent technologies in the ?eld of bio-sciences, high-energy physics, economy, climate analysis, and in several other scienti?c disciplines, requires a new generation of machine learning and statistical methods to deal with their complexity and hete- geneity. As data collections becomes easier, data analysis is required to be more sophisticated in order to extract useful information from the available data. Even if data can be represented in several ways, according to their structural characteristics, ranging from strings, lists, trees to graphs and other more complex data structures, in most applications they are typically represented as a matrix whose rows correspond to measurable characteristics called f- tures, attributes, variables, depending on the considered discipline and whose columns correspond to examples (cases, samples, patterns). In order to avoid confusion,we will talk about features and examples.In real-worldtasks,there canbe manymorefeatures than examples(cancer classi?cationbasedongene expressionlevels in bioinformatics) or there can be many more examples than features(intrusion detection in computer/networksecurity).In addition, each example can be either labeled or not. Attaching labels allows to distinguish members of the same class or group from members of other classes or groups. Hence, one can talk about supervised and unsupervised tasks that can be solved by machine learning methods. Since it is widely accepted that no single classi?er or clustering algorithm canbesuperiortotheothers,ensemblesofsupervisedandunsupervisedme- ods are gaining popularity. A typical ensemble includes a number of clas- ?ers/clustererswhosepredictionsarecombinedtogetheraccordingtoacertain rule, e.g. majority vote. This book results from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) in June 2007 in Girona, Spain. This workshop was held alongside the 3rd Iberian Conference on Pattern Recognition and Image Analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Buch. Condizione: Neu. Neuware -majority vote.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 196 pp. Englisch.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book was inspired by the last argument and resulted from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) organized on June 4, 2007 in Girona, Spain. This workshop was held in conjunction with the 3rd Iberian Conference on Pattern Recognition and Image Analysis and was intended to encompass the progress in the ensemble applications made by the Iberian and international scholars. Despite its small format, SUEMA attracted researchers from Spain, Portugal, France, USA, Italy, and Finland, who presented interesting ideas about using the ensembles in various practical cases. Encouraged by this enthusiastic reply, we decided to publish workshop papers in an edited book, since CD proceedings were the only media distributed among the workshop participants at that time. The book includes nine chapters divided into two parts, assembling contributions to the applications of supervised and unsupervised ensembles.The book is intended to be primarily a reference work. It could be a good complement to two excellent books on ensemble methodology - 'Combining pattern classifiers: methods and algorithms' by Ludmila Kuncheva (John Wiley & Sons, 2004) and 'Decomposition methodology for knowledge discovery and data mining: theory and applications' by Oded Maimon and Lior Rokach (World Scientific, 2005).

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Hardcover. Condizione: new. Hardcover. The rapidly growing amount of data, available from di?erent technologies in the ?eld of bio-sciences, high-energy physics, economy, climate analysis, and in several other scienti?c disciplines, requires a new generation of machine learning and statistical methods to deal with their complexity and hete- geneity. As data collections becomes easier, data analysis is required to be more sophisticated in order to extract useful information from the available data. Even if data can be represented in several ways, according to their structural characteristics, ranging from strings, lists, trees to graphs and other more complex data structures, in most applications they are typically represented as a matrix whose rows correspond to measurable characteristics called f- tures, attributes, variables, depending on the considered discipline and whose columns correspond to examples (cases, samples, patterns). In order to avoid confusion,we will talk about features and examples.In real-worldtasks,there canbe manymorefeatures than examples(cancer classi?cationbasedongene expressionlevels in bioinformatics) or there can be many more examples than features(intrusion detection in computer/networksecurity).In addition, each example can be either labeled or not. Attaching labels allows to distinguish members of the same class or group from members of other classes or groups. Hence, one can talk about supervised and unsupervised tasks that can be solved by machine learning methods. Since it is widely accepted that no single classi?er or clustering algorithm canbesuperiortotheothers,ensemblesofsupervisedandunsupervisedme- ods are gaining popularity. A typical ensemble includes a number of clas- ?ers/clustererswhosepredictionsarecombinedtogetheraccordingtoacertain rule, e.g. majority vote. This book results from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) in June 2007 in Girona, Spain. This workshop was held alongside the 3rd Iberian Conference on Pattern Recognition and Image Analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book was inspired by the last argument and resulted from the workshop on Supervised and Unsupervised Ensemble Methods and their Applications (briefly, SUEMA) organized on June 4, 2007 in Girona, Spain. This workshop was held in conjunction with the 3rd Iberian Conference on Pattern Recognition and Image Analysis and was intended to encompass the progress in the ensemble applications made by the Iberian and international scholars. Despite its small format, SUEMA attracted researchers from Spain, Portugal, France, USA, Italy, and Finland, who presented interesting ideas about using the ensembles in various practical cases. Encouraged by this enthusiastic reply, we decided to publish workshop papers in an edited book, since CD proceedings were the only media distributed among the workshop participants at that time. The book includes nine chapters divided into two parts, assembling contributions to the applications of supervised and unsupervised ensembles.The book is intended to be primarily a reference work. It could be a good complement to two excellent books on ensemble methodology - 'Combining pattern classifiers: methods and algorithms' by Ludmila Kuncheva (John Wiley & Sons, 2004) and 'Decomposition methodology for knowledge discovery and data mining: theory and applications' by Oded Maimon and Lior Rokach (World Scientific, 2005). 196 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent developments of Supervised and Unsupervised Ensemble Methods and Their ApplicationsExtended contributions from SUEMA 2007 workshop and moreEnsembles of Clustering Methods and Their Applications.- Cluster Ensemble Methods: f.