IDEAL 2013 Special Session on Combining Learning and Optimisation for Intelligent Data Engineering
Call for Papers
Aims & Scope
of Machine Learning and Optimisation are workhorses in intelligent data
engineering and in today's emerging data science. Finding ways to combine
learning with optimisation has tremendous potential to provide powerful
computational intelligence techniques. In fact, optimisation is a key in many
machine learning and data mining algorithms; at the same time optimisation
methods that incorporate some form of learning strategy have an added level of
sophistication and ability to explore large search spaces.
This special session aims at exploring new synergies and multi-disciplinary perspectives between optimisation and machine learning in the context of intelligent data engineering and large scale data mining problems.
Topics of interest include, but are not limited to the following:
- Fundamentals, hypotheses, and new challenges in data science
- Model building optimisation algorithms, estimation of distribution algorithms
- Dimensionality reduction for large scale learning and optimisation
- New ways to hybridise learning and optimisation
- Compressive representations and randomisation for large scale problems
- Practical systems and real world applications of hybrid optimisation and machine learning methods
Frank-Michael Schleif, Xibin Zhu and Barbara Hammer
Edgar Reehuis, Markus Olhofer, Bernhard Sendhoff and Thomas Back
Lili Zhuang, Ke Tang and Yaochu Jin
Sakinah Ali Pitchay and Ata Kaban
Jakramate Bootkrajang and Ata Kaban
High quality original submissions are solicited for presentation at the Special Session. Papers should not exceed 8 pages and must be formatting instructions of the main conference. All submissions will be peer-reviewed, and all accepted papers will be included in IDEAL 2013 Proceedings published by Springer in the LNCS series.
Submission deadline: 17 June 2013 (extended!).
copy & early registration:
Conference: 20-23 October 2013.
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