okun oleg valentini giorgio (30 résultats)

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

Etat : New.

Etat : New.

Etat : New.

Etat : New. pp. 196.

Etat : New. In.

Etat : New. In.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : Sehr gut. Zustand: Sehr gut | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

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

Etat : New.

Gebunden. Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : 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 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.

Hardcover. Etat : Brand New. 272 pages. 9.25x6.25x1.00 inches. In Stock.

Hardcover. Etat : Brand New. 1st edition. 268 pages. 9.25x6.50x1.00 inches. In Stock.

Buch. Etat : Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch.

Taschenbuch. Etat : 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.

Buch. Etat : 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.

Taschenbuch. Etat : 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 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. 276 pp. Englisch.

Buch. Etat : 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 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. 272 pp. Englisch.

Etat : New. Print on Demand pp. 272.

Etat : 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 2008 workshop and moreAn Ensemble Pruning Primer.- Evade Hard Multiple Classifier Systems.- A Personal Ant.

Etat : 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.

Etat : New. PRINT ON DEMAND pp. 272.

Taschenbuch. Etat : 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.

Buch. Etat : Neu. Ensembles in Machine Learning Applications | Oleg Okun (u. a.) | Buch | xx | Englisch | 2011 | Springer | EAN 9783642229091 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Taschenbuch. Etat : Neu. Ensembles in Machine Learning Applications | Oleg Okun (u. a.) | Taschenbuch | 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 Print on Demand.