Machine Learning and Knowledge Discovery in Databases
Editat de Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip ¿Eleznýen Limba Engleză Paperback – 12 sep 2013
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Specificații
ISBN-13: 9783642409875
ISBN-10: 3642409873
Pagini: 748
Ilustrații: LIV, 691 p. 198 illus.
Dimensiuni: 155 x 235 x 40 mm
Greutate: 1.11 kg
Ediția:2013
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
ISBN-10: 3642409873
Pagini: 748
Ilustrații: LIV, 691 p. 198 illus.
Dimensiuni: 155 x 235 x 40 mm
Greutate: 1.11 kg
Ediția:2013
Editura: Springer
Locul publicării:Berlin, Heidelberg, Germany
Public țintă
ResearchCuprins
Reinforcement learning.- Markov decision processes.- Active learning and optimization.- Learning from sequences.- Time series and spatio-temporal data.- Data streams.- Graphs and networks.- Social network analysis.- Natural language processing and information extraction.- Ranking and recommender systems.- Matrix and tensor analysis.- Structured output prediction, multi-label and multi-task learning.- Transfer learning.- Bayesian learning.- Graphical models.- Nearest-neighbor methods.- Ensembles.- Statistical learning.- Semi-supervised learning.- Unsupervised learning.- Subgroup discovery, outlier detection and anomaly detection.- Privacy and security.- Evaluation.- Applications.- Medical applications.
Textul de pe ultima copertă
This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; medical applications; nectar track; demo track.
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