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Machine Learning and Knowledge Discovery in Databases

European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II

Specificaties
Paperback, 693 blz. | Engels
Springer Berlin Heidelberg | 2013e druk, 2013
ISBN13: 9783642409905
Rubricering
Springer Berlin Heidelberg 2013e druk, 2013 9783642409905
Onderdeel van serie Lecture Notes in Computer Science
Verwachte levertijd ongeveer 9 werkdagen

Samenvatting

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; and medical applications.

Specificaties

ISBN13:9783642409905
Taal:Engels
Bindwijze:paperback
Aantal pagina's:693
Uitgever:Springer Berlin Heidelberg
Druk:2013

Inhoudsopgave

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.

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        Machine Learning and Knowledge Discovery in Databases