{Эволюционные вычисления} Fw: [ML-news] Call for Book Chapters - Evolutionary Kernel Machines
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Andrey Gavrilov
Apr 24, 2010, 9:32:35 PM4/24/10
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----- Original Message -----
From: Oliver
Kramer
Sent: Wednesday, April 21, 2010 4:48 AM
Subject: [ML-news] Call for Book Chapters - Evolutionary Kernel
Machines
CALL FOR BOOK
CHAPTERS
-- Evolutionary Kernel Machines --
Springer, Series: Studies in Computational Intelligence
EDITORS
Dr. Oliver Kramer, International Computer Science Institute, Berkeley,
email: oliver...@icsi.berkeley.edu
Prof. Dr. Günter Rudolph, Fakultät für Informatik, Technische Universität Dortmund,
email: guenter...@tu-dortmund.de
Dr. Christian Igel, Institut für Neuroinformatik, Ruhr-Universität Bochum,
email: christi...@neuroinformatik.rub.de
CONTENT
Kernel-based techniques are state of the art in machine learning. The various optimization problems that arise when applying kernel methods are usually solved using deterministic mathematical programming techniques. However, there are good reasons to employ stochastic optimization in machine learning because one faces problems that suffer from numerous local optima, crucial parameter dependencies, discontinuous objective functions, and/or noise. Stochastic search methods may help to overcome these problems. In particular, evolutionary computation has become a rich field for powerful methods in global optimization. They are embarrassingly parallelizable and thus fairly efficient search methodologies in distributed computing scenarios. Successful heuristic extensions have been proposed for special solution space conditions such as multi-objective, multi-modal, and constrained objective functions. These developments motivate the application of advanced evolutionary optimizers to kernel machines. Meanwhile, many results have shown that this is a fruitful union. This edited book invites papers on all aspects of theory, computation, and application related topics on the intersection of evolutionary computation and kernel machines. The goal of the book is not to be another collection of research papers, but to comprise deep and self-contained chapters representative in their field, and to give a comprehensive insight into evolutionary kernel machines. The topics may cover the following fields:
- Evolutionary and stochastic optimization (evolution strategies, particle swarm optimization, hybrid meta-heuristics, etc.) in kernel-based machine learning (classification, regression, clustering, dimension reduction, manifold learning)
- Evolutionary engines, e.g., embedded optimization problems, for kernel machines
- Stochastic search for model selection, e.g., for adaptation of kernel- and hyper-parameters
- Balancing of conflicting objectives with evolutionary multi-objective optimization methods
- Online-tuning and -adaptation of kernel-based machine learning parameters
- Design of experiments and offline-tuning in kernel-based machine learning
- Machine learning in optimization, e.g., meta-modeling with regression techniques or clustering as post-analysis
- Parallelization of evolutionary optimizers for kernel-based learning
- Applications of evolutionary kernel-based machine learning algorithms
- Theoretical analyses, e.g., runtime or convergence analyses for evolutionary kernel machines
To make sure that your work fits into the project it is recommended to send a a short abstract of your planned contribution in advance.
IMPORTANT DATES
Submission deadline:
October 1, 2010
Notification of the first review:
January 15, 2011
Revisions due:
March 1, 2011
Final manuscript:
April 2011
Publication:
Summer 2011
Manuscripts should be prepared according to the Springer style for monographs, see http://www.springer.com/computer/lncs/ for more information.
Submissions should be done via email to the editors.
-- Evolutionary Kernel Machines --
Springer, Series: Studies in Computational Intelligence
EDITORS
Dr. Oliver Kramer, International Computer Science Institute, Berkeley,
email: oliver...@icsi.berkeley.edu
Prof. Dr. Günter Rudolph, Fakultät für Informatik, Technische Universität Dortmund,
email: guenter...@tu-dortmund.de
Dr. Christian Igel, Institut für Neuroinformatik, Ruhr-Universität Bochum,
email: christi...@neuroinformatik.rub.de
CONTENT
Kernel-based techniques are state of the art in machine learning. The various optimization problems that arise when applying kernel methods are usually solved using deterministic mathematical programming techniques. However, there are good reasons to employ stochastic optimization in machine learning because one faces problems that suffer from numerous local optima, crucial parameter dependencies, discontinuous objective functions, and/or noise. Stochastic search methods may help to overcome these problems. In particular, evolutionary computation has become a rich field for powerful methods in global optimization. They are embarrassingly parallelizable and thus fairly efficient search methodologies in distributed computing scenarios. Successful heuristic extensions have been proposed for special solution space conditions such as multi-objective, multi-modal, and constrained objective functions. These developments motivate the application of advanced evolutionary optimizers to kernel machines. Meanwhile, many results have shown that this is a fruitful union. This edited book invites papers on all aspects of theory, computation, and application related topics on the intersection of evolutionary computation and kernel machines. The goal of the book is not to be another collection of research papers, but to comprise deep and self-contained chapters representative in their field, and to give a comprehensive insight into evolutionary kernel machines. The topics may cover the following fields:
- Evolutionary and stochastic optimization (evolution strategies, particle swarm optimization, hybrid meta-heuristics, etc.) in kernel-based machine learning (classification, regression, clustering, dimension reduction, manifold learning)
- Evolutionary engines, e.g., embedded optimization problems, for kernel machines
- Stochastic search for model selection, e.g., for adaptation of kernel- and hyper-parameters
- Balancing of conflicting objectives with evolutionary multi-objective optimization methods
- Online-tuning and -adaptation of kernel-based machine learning parameters
- Design of experiments and offline-tuning in kernel-based machine learning
- Machine learning in optimization, e.g., meta-modeling with regression techniques or clustering as post-analysis
- Parallelization of evolutionary optimizers for kernel-based learning
- Applications of evolutionary kernel-based machine learning algorithms
- Theoretical analyses, e.g., runtime or convergence analyses for evolutionary kernel machines
To make sure that your work fits into the project it is recommended to send a a short abstract of your planned contribution in advance.
IMPORTANT DATES
Submission deadline:
October 1, 2010
Notification of the first review:
January 15, 2011
Revisions due:
March 1, 2011
Final manuscript:
April 2011
Publication:
Summer 2011
Manuscripts should be prepared according to the Springer style for monographs, see http://www.springer.com/computer/lncs/ for more information.
Submissions should be done via email to the editors.
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