TY  - BOOK
AU  - Calders,Toon
AU  - Esposito,Floriana
AU  - Hüllermeier,Eyke
AU  - Meo,Rosa
ED  - SpringerLink (Online service)
TI  - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II 
T2  - Lecture Notes in Computer Science,
SN  - 9783662448519
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2014///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg, Imprint: Springer
KW  - Computer science
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Artificial intelligence
KW  - Optical pattern recognition
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Artificial Intelligence (incl. Robotics)
KW  - Pattern Recognition
KW  - Information Storage and Retrieval
N1  - Dynamic networks and knowledge discovery -- Interactions between data mining and natural language processing -- Mining ubiquitous and social environments -- Statistically sound data mining -- Machine learning for urban sensor data -- Multi-target prediction -- Representation learning -- Neural connectomics: from imaging to connectivity -- Data analytics for renewable energy integration -- Linked data for knowledge discovery -- New frontiers in mining complex patterns -- Experimental economics and machine learning -- Learning with multiple views: applications to computer vision and multimedia mining -- Generalization and reuse of machine learning models over multiple contexts.- Predictive web analytics
N2  - This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases
UR  - http://dx.doi.org/10.1007/978-3-662-44851-9
ER  -