Band 10536
Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III
Aus der Reihe
Lecture Notes in Artificial Intelligence
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- Taschenbuch ausgewählt
- eBook
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Sprache:Englisch
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Verlag:Springer
- Springer 51,99 € ausgewählt
- Springer Berlin 51,99 €
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Auflage:1st edition 2017
51,99 €
inkl. gesetzl. MwSt.,
Beschreibung
Produktdetails
Einband
Taschenbuch
Erscheinungsdatum
30.12.2017
Abbildungen
XXXV, 144 illus., schwarz-weiss Illustrationen
Herausgeber
Yasemin Altun + weitereVerlag
SpringerSeitenzahl
448
Maße (L/B/H)
23,5/15,5/2,7 cm
Gewicht
727 g
Auflage
1st edition 2017
Sprache
Englisch
ISBN
978-3-319-71272-7
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.
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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