Ensembles machine learning applications (30 risultati)
Ensembles in machine learning applications. Studies in computational intelligence; Vol. 373.
Okun, Oleg [Hrsg.], Giorgio [Hrsg.] Valentini and Matteo [Hrsg.] Re:
Lingua: Inglese
Editore: Berlin ; Heidelberg : Springer, 2011
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OPp., gebundene Ausgabe. Condizione: Befriedigend. XX, 252 S.: Ill., graph. Darst. ; 24 cm, Einband berieben. ISBN: 9783642229091 Sprache: Englisch Gewicht in Gramm: 680.
Ensembles in Machine Learning Applications
Okun, Oleg (EDT); Valentini, Giorgio (EDT); Re, Matteo (EDT)
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Ensembles in Machine Learning Applications
Okun, Oleg (EDT); Valentini, Giorgio (EDT); Re, Matteo (EDT)
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Ensembles in Machine Learning Applications
Okun, Oleg (EDT); Valentini, Giorgio (EDT); Re, Matteo (EDT)
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Ensembles in Machine Learning Applications
Okun, Oleg (EDT); Valentini, Giorgio (EDT); Re, Matteo (EDT)
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Lingua: Inglese
Editore: Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2016
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Paperback. Condizione: New. Softcover reprint of the original 1st ed. 2011.
Lingua: Inglese
Editore: Springer, 2011
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Condizione: New. pp. 272 Softcover reprint of the original 1st ed. 2011 edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Springer Berlin Heidelberg, 2011
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Lingua: Inglese
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Condizione: New.
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Editore: J.B. Metzler, 2016
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Condizione: Gut. Zustand: Gut | Seiten: 276 | Sprache: Englisch | Produktart: Bücher | 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 an…d 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.
Altre immaginiLingua: Inglese
Editore: Springer, 2016
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Taschenbuch. Condizione: Neu. Ensembles in Machine Learning Applications | Oleg Okun (u. a.) | Taschenbuch | Studies in Computational Intelligence | xx | Englisch | 2016 | Springer | EAN 9783662507063 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[…dot]com | Anbieter: preigu.
Ensembles in Machine Learning Applications
Okun, Oleg (Editor)/ Valentini, Giorgio (Editor)/ Re, Matteo (Editor)
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2011
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Hardcover. Condizione: Brand New. 272 pages. 9.25x6.25x1.00 inches. In Stock.
Lingua: Inglese
Editore: Springer Berlin Heidelberg, 2011
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Buch. Condizione: 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.
Lingua: Inglese
Editore: Springer, Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
- Brossura
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: 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 Pr…actice 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.
Lingua: Inglese
Editore: Springer, 2011
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
- Rilegato
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Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | 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 Pri…nciples 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.
Lingua: Inglese
Editore: Springer-Verlag Berlin and Heidelberg GmbH and Co. KG, DE, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Paperback. Condizione: New. Softcover reprint of the original 1st ed. 2011.
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Editore: J.B. Metzler, 2016
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Softcover. Condizione: gut. 2016. Ensembles in Machine Learning Applications In deutscher Sprache. pages.
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Editore: Springer, 2011
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Ensembles in Machine Learning Applications (Studies in Computational Intelligence)
Okun, Oleg (Editor) / Re, Matteo (Editor) / Valentini, Giorgio (Editor)
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Paperback. Condizione: Brand New. reprint edition. 252 pages. 9.25x6.10x0.65 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Springer Berlin Heidelberg Sep 2011, 2011
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 Principl…es 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. 272 pp. Englisch.
Lingua: Inglese
Editore: Springer Berlin Heidelberg Aug 2016, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
- Brossura
- Print on Demand
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 P…rinciples 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. 276 pp. Englisch.
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Condizione: New. Print on Demand pp. 272.
Lingua: Inglese
Editore: Springer, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Condizione: New. PRINT ON DEMAND pp. 272.
Lingua: Inglese
Editore: Springer, Springer Aug 2016, 2016
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methodsand their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning andPrincip…les 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 orcluster 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 twoprevious SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/orprogramming code of the algorithms described in them. This was done in order to facilitate ensemble adoption inpractice and to help to both researchers and engineers developing ensemble applications.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 276 pp. Englisch.
Lingua: Inglese
Editore: Springer, Springer Sep 2011, 2011
Serie: Studies in Computational Intelligence, Libro 28 di 538. Libro 28 di 538 - Studies in Computational Intelligence
- Rilegato
- Print on Demand
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 106,99
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methodsand their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning andPrinciples 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 orcluster 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 twoprevious SUEMA editions, also published by Springer, many chapters in the current book include pseudo code and/orprogramming code of the algorithms described in them. This was done in order to facilitate ensemble adoption inpractice and to help to both researchers and engineers developing ensemble applications.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.
