| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNISA996200359603316 |
|
|
Titolo |
Machine Learning and Knowledge Discovery in Databases [[electronic resource] ] : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II / / edited by Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Edizione |
[1st ed. 2015.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (XLII, 773 p. 198 illus.) |
|
|
|
|
|
|
Collana |
|
Lecture Notes in Artificial Intelligence ; ; 9285 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Data mining |
Artificial intelligence |
Pattern recognition |
Information storage and retrieval |
Database management |
Application software |
Data Mining and Knowledge Discovery |
Artificial Intelligence |
Pattern Recognition |
Information Storage and Retrieval |
Database Management |
Information Systems Applications (incl. Internet) |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Note generali |
|
Bibliographic Level Mode of Issuance: Monograph |
|
|
|
|
|
|
Sommario/riassunto |
|
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. |
|
|
|
|
|
| |