Okun oleg (95 resultados)

OPp., gebundene Ausgabe. Condición: Befriedigend. XX, 252 S.: Ill., graph. Darst. ; 24 cm, Einband berieben. ISBN: 9783642229091 Sprache: Englisch Gewicht in Gramm: 680.

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Condición: Used. pp. 196.

Condición: Used. 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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Condición: Used. pp. 196.

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

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Paperback. Condición: New. This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms - advanced machinelearning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a groupof algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications. Softcover reprint of the original 1st ed. 2011.…

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Condición: New. In English.

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Gebunden. Condición: New.

Condición: New. pp. 460.

Condición: New. Presents recent developments of Supervised and Unsupervised Ensemble Methods and Their ApplicationsExtended contributions from SUEMA 2008 workshop and moreAn Ensemble Pruning Primer.- Evade Hard Multiple Classifier Systems.- A Personal Ant.

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Condición: New. pp. 196.

Condición: New. pp. 272 Softcover reprint of the original 1st ed. 2011 edition NO-PA16APR2015-KAP.

Condición: New. pp. 292.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2010, Barcelona, Catalonia, Spain). As its two predecessors, its main theme was ensembles of supervised and unsupervised algorithms - advanced machinelearning and data mining technique. Unlike a single classification or clustering algorithm, an ensemble is a groupof algorithms, each of which first independently solves the task at hand by assigning a class or cluster label (voting) to instances in a dataset and after that all votes are combined together to produce the final class or cluster membership. As a result, ensembles often outperform best single algorithms in many real-world problems. This book consists of 14 chapters, each of which can be read independently of the others. In addition to two previous SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/or programming code of the algorithms described in them. This was done in order to facilitate ensemble adoption in practice and to help to both researchers and engineers developing ensemble applications.…