[Comp-neuro] IEEE Computational Intelligence Magazine (CIM) Special Issue: “Model Complexity, Regularization and Sparsity”

Giacomo Boracchi boracchi at elet.polimi.it
Sun Jul 26 23:13:22 CEST 2015

*Call for Papers*

*IEEE Computational Intelligence Magazine*

*Special Issue on “Model Complexity, Regularization and Sparsity”*


*Aims and Scope*

The effective management of solution complexity is one of the most
important issues in addressing Computational Intelligence problems.
Regularization techniques control model complexity by taking advantage of
some prior information regarding the problem at hand, represented as
penalty expressions that impose these properties on the solution. Over the
past few years, one of the most prominent and successful types of
regularization has been based on the *sparsity* prior, which promotes
solutions that can be expressed as a linear combination of a few atoms
belonging to a *dictionary*. Sparsity can in some sense be considered a
“measure of simplicity“ and, as such, is compatible with many
nature-inspired principles of Computational Intelligence. Nowadays,
sparsity has become one of the leading approaches for learning adaptive
representations for both descriptive and discriminative tasks, and has been
shown to be particularly effective when dealing with structured, complex
and high-dimensional data.

Regularization, including sparsity and other priors to control the model
complexity, is often the key ingredient in the successful solution of
difficult problems; it is therefore not surprising that these aspects have
also recently gained a lot of attention in big-data processing, due to
unprecedented challenges associated with the need to handle massive
datastreams that are possibly high-dimensional and organized in complex

This special issue aims at presenting the most relevant regularization
techniques and approaches to control model complexity in Computational
Intelligence. Submissions of papers presenting regularization methods for
Neural Networks, Evolutionary Computation or Fuzzy Systems, are welcome.
Submissions of papers presenting advanced regularization techniques in
specific, but relevant, application fields such as data/datastream-mining,
classification, big-data analytics, image/signal analysis, natural-language
processing, are also encouraged.

*Topics of Interest*

·           Regularization methods for big and high-dimensional data;

·           Regularization methods for supervised and unsupervised learning;

·           Regularization methods for ill-posed problems in Computational

·           Techniques to control model complexity;

·           Sparse representations in Computational Intelligence;

·           Managing model complexity in data analytics;

·           Effective priors for solving Computational Intelligence

·           Multiple prior integration;

·           Regularization in kernel methods and support vector machines.

*Important Dates*

·           22nd January, 2016: Submission of Manuscripts

·           30th March, 2016: Notification of Review Results

·           30th April, 2016: Submission of Revised Manuscripts

·           15th June, 2016: Submission of Final Manuscripts

·           November, 2016: Special Issue Publication

*Submission Process*

The maximum length for the manuscript is typically 20 pages in single
column with double-spacing, including figures and references. Authors of
papers should specify in the first page of their manuscripts the
corresponding author’s contact and up to 5 keywords. Additional information
about submission guidelines and information for authors is provided at the IEEE
CIM website.

Submission instructions can be found at

*Guest Editors*

*Prof. Cesare Alippi,*

Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di

via Ponzio 34/5, Milano, 20133, Italy

email: cesare.alippi at polimi.it

*Dr. Giacomo Boracchi*,

Dipartimento di Elettronica, Informazione e Biongegneria, Politecnico di

via Ponzio 34/5, Milano, 20133, Italy

email: giacomo.boracchi at polimi.it

*Dr. Brendt Wohlberg*,

Theoretical Division, Los Alamos National Laboratory,

Los Alamos NM 87545, USA

email: brendt at lanl.gov

Giacomo Boracchi, PhD
DEIB - Dipartimento di Elettronica,  Informazione e Bioingegneria
Politecnico di Milano
Via Ponzio, 34/5 20133 Milano, Italy.
Tel. +39 02 2399 3467

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