Description:
This workshop will provide a platform for discussing the recent developments in the area of algorithm selection and configuration which arises in many diverse domains, such as machine learning, data mining, optimization and satisfiability solving. Algorithm Selection and configuration are increasingly relevant today. Researchers and practitioners from all branches of science and technology face a large choice of parameterized machine learning algorithms, with little guidance as to which techniques to use. Moreover, data mining challenges frequently remind us that algorithm selection and configuration are crucial in order to achieve the best performance, and drive industrial applications. Meta-learning leverages knowledge of past algorithm applications to select the best techniques for future applications, and offers effective techniques that are superior to humans both in terms of the end result and especially in the time required to achieve it. In this workshop we will discuss different ways of exploiting meta-learning techniques to identify the potentially best algorithm(s) for a new task, based on meta-level information and prior experiments. We also discuss the prerequisites for effective meta-learning systems, for example infrastructure such as OpenML.org.Many contemporary problems also require that solutions be elaborated in the form of complex systems or workflows which include many different processes or operations. Constructing such complex systems or workflows requires extensive expertise, and could be greatly facilitated by leveraging planning, meta-learning and intelligent system design. This task is inherently interdisciplinary, as it builds on expertise in various areas of AI.
Website:http://metasel2015.inesctec.pt/
Workshop Organizers:
Pavel Brazdil
Joaquin Vanschoren
Lars Kotthoff
Christophe Giraud-Carrier
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