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Showing 1 - 4 of
4 matches in All Departments
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Advances in Intelligent Data Analysis XI - 11th International Symposium, IDA 2012, Helsinki, Finland, October 25-27, 2012, Proceedings (Paperback, 2012)
Jaakko Hollmen, Frank Klawonn, Allan Tucker
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R1,447
Discovery Miles 14 470
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Ships in 18 - 22 working days
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This book constitutes the refereed proceedings of the 11th
International Conference on Intelligent Data Analysis, IDA 2012,
held in Helsinki, Finland, in October 2012. The 32 revised full
papers presented together with 3 invited papers were carefully
reviewed and selected from 88 submissions. All current aspects of
intelligent data analysis are addressed, including intelligent
support for modeling and analyzing data from complex, dynamical
systems. The papers focus on novel applications of IDA techniques
to, e.g., networked digital information systems; novel modes of
data acquisition and the associated issues; robustness and
scalability issues of intelligent data analysis techniques; and
visualization and dissemination results.
This book constitutes the refereed proceedings of the 14th
International Conference on Discovery Science, DS 2011, held in
Espoo, Finland, in October 2011 - co-located with ALT 2011, the
22nd International Conference on Algorithmic Learning Theory. The
24 revised full papers presented together with 5 invited lectures
were carefully revised and selected from 56 submissions. The papers
cover a wide range including the development and analysis of
methods for automatic scientific knowledge discovery, machine
learning, intelligent data analysis, theory of learning, as well as
their application to knowledge discovery.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II (Paperback, 1st ed. 2017)
Michelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
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R1,570
Discovery Miles 15 700
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Ships in 18 - 22 working days
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The three volume proceedings LNAI 10534 - 10536 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2017, held in
Skopje, Macedonia, in September 2017. The total of 101 regular
papers presented in part I and part II was carefully reviewed and
selected from 364 submissions; there are 47 papers in the applied
data science, nectar and demo track. The contributions were
organized in topical sections named as follows: Part I: anomaly
detection; computer vision; ensembles and meta learning; feature
selection and extraction; kernel methods; learning and
optimization, matrix and tensor factorization; networks and graphs;
neural networks and deep learning. Part II: pattern and sequence
mining; privacy and security; probabilistic models and methods;
recommendation; regression; reinforcement learning; subgroup
discovery; time series and streams; transfer and multi-task
learning; unsupervised and semisupervised learning. Part III:
applied data science track; nectar track; and demo track.
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Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part I (Paperback, 1st ed. 2017)
Michelangelo Ceci, Jaakko Hollmen, Ljupco Todorovski, Celine Vens, Saso Dzeroski
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R1,575
Discovery Miles 15 750
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Ships in 18 - 22 working days
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The three volume proceedings LNAI 10534 - 10536 constitutes the
refereed proceedings of the European Conference on Machine Learning
and Knowledge Discovery in Databases, ECML PKDD 2017, held in
Skopje, Macedonia, in September 2017. The total of 101 regular
papers presented in part I and part II was carefully reviewed and
selected from 364 submissions; there are 47 papers in the applied
data science, nectar and demo track. The contributions were
organized in topical sections named as follows: Part I: anomaly
detection; computer vision; ensembles and meta learning; feature
selection and extraction; kernel methods; learning and
optimization, matrix and tensor factorization; networks and graphs;
neural networks and deep learning. Part II: pattern and sequence
mining; privacy and security; probabilistic models and methods;
recommendation; regression; reinforcement learning; subgroup
discovery; time series and streams; transfer and multi-task
learning; unsupervised and semisupervised learning. Part III:
applied data science track; nectar track; and demo track.
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