Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III: 10536 (Lecture Notes in Computer Science) - Tapa blanda

9783319712727: Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III: 10536 (Lecture Notes in Computer Science)

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ISBN 10: 3319712721 ISBN 13: 9783319712727

Editorial: Springer, 2017

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The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.

The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.

The contributions were organized in topical sections named as follows:
Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning.
Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning.
Part III: applied data science track; nectar track; and demo track.

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EditorialSpringer
A�o de publicaci�n2017
ISBN 10 3319712721
ISBN 13 9783319712727
Encuadernaci�nTapa blanda
N�mero de edici�n1
N�mero de p�ginas484
EditorAltun Yasemin, Das Kamalika, Mielik�inen Taneli, Malerba Donato, Stefanowski Jerzy, Read Jesse, Žitnik Marinka, Ceci Michelangelo, Džeroski Sašo

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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 1822, 2017, Proceedings, Part III (Lecture Notes in Computer Science) [Paperback ]

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Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18?22, 2017, Proceedings, Part III (Lecture Notes in Artificial Intelligence)

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Machine Learning and Knowledge Discovery in Databases : European Conference, Ecml Pkdd 2017, Skopje, Macedonia, September 18?22, 2017, Proceedings, Part III

Ceci, Michelangelo (EDT); Hollmen, Jaakko (EDT); Todorovski, Ljupco (EDT); Vens, Celine (EDT); D?eroski, Sa?o (EDT)

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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part III

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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18?22, 2017, Proceedings, Part III

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Machine Learning and Knowledge Discovery in Databases

Yasemin Altun

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Descripci�n Taschenbuch. Condici�n: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The three volume proceedings LNAI 10534 - 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017.The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, nectar and demo track.The contributions were organized in topical sections named as follows: Part I: anomaly detection; computer vision; ensembles and meta learning; feature selection and extraction; kernel methods; learning and optimization, matrix and tensor factorization; networks and graphs; neural networks and deep learning. Part II: pattern and sequence mining; privacy and security; probabilistic models and methods; recommendation; regression; reinforcement learning; subgroup discovery; time series and streams; transfer and multi-task learning; unsupervised and semisupervised learning. Part III: applied data science track; nectar track; and demo track. 484 pp. Englisch. N� de ref. del art�culo: 9783319712727

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