Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II

Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares (Herausgeber)

Buch | Softcover

XLII, 773 Seiten

2015 | 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-23524-0 (ISBN)

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The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015.The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Erscheint lt. Verlag	14.9.2015
Reihe/Serie	Lecture Notes in Artificial Intelligence
Reihe/Serie	Lecture Notes in Computer Science
Zusatzinfo	XLII, 773 p. 198 illus.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Datenbanken ► Data Warehouse / Data Mining
Schlagworte	Applications • classification, regression and supervised learning • clustering and unsupervised learning • Computer Science • conference proceedings • Data Mining • data mining and knowledge discovery • domain adaptation • ensemble learning • foundations of machine learning and data mining • Informatics • Knowledge Discovery in Databases • large scale learning and big data • Learning Paradigms • machine learning and data mining applications • machine learning methodologies • Meta-learning • nonmonotonic constraints • pattern and sequence mining • privacy-preserving data mining • probabilistic models and statistical methods • probabilistic programming • Recommender Systems • Research • rich data mining • social and graphs mining
ISBN-10	3-319-23524-9 / 3319235249
ISBN-13	978-3-319-23524-0 / 9783319235240
Zustand	Neuware