Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012 : proceedings
著者
書誌事項
Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012 : proceedings
（Lecture notes in computer science, 7523-7524 . Lecture notes in artificial intelligence）
Springer, c2012
- pt. 1
- pt. 2
- タイトル別名
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ECML PKDD 2012
大学図書館所蔵 件 / 全2件
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該当する所蔵館はありません
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注記
Includes bibliographical references and author indexes
内容説明・目次
- 巻冊次
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pt. 1 ISBN 9783642334597
内容説明
This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
目次
Aassociation rules and frequent patterns.- Bayesian learning and graphical models.- classification.- dimensionality reduction, feature selection and extraction.- distance-based methods and kernels.- ensemble methods.- graph and tree mining.- large-scale, distributed and parallel mining and learning.- multi-relational mining and learning.- multi-task learning.- natural language processing.- online learning and data streams.
- 巻冊次
-
pt. 2 ISBN 9783642334856
内容説明
This two-volume set LNAI 7523 and LNAI 7524 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2012, held in Bristol, UK, in September 2012. The 105 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 443 submissions. The final sections of the proceedings are devoted to Demo and Nectar papers. The Demo track includes 10 papers (from 19 submissions) and the Nectar track includes 4 papers (from 14 submissions). The papers grouped in topical sections on association rules and frequent patterns; Bayesian learning and graphical models; classification; dimensionality reduction, feature selection and extraction; distance-based methods and kernels; ensemble methods; graph and tree mining; large-scale, distributed and parallel mining and learning; multi-relational mining and learning; multi-task learning; natural language processing; online learning and data streams; privacy and security; rankings and recommendations; reinforcement learning and planning; rule mining and subgroup discovery; semi-supervised and transductive learning; sensor data; sequence and string mining; social network mining; spatial and geographical data mining; statistical methods and evaluation; time series and temporal data mining; and transfer learning.
目次
Privacy and security.- rankings and recommendations.- reinforcement learning and planning.- rule mining and subgroup discovery.- semi-supervised and transductive learning.- sensor data.- sequence and string mining.- social network mining.- spatial and geographical data mining.- statistical methods and evaluation.- time series and temporal data mining.- transfer learning.
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