Connectionists: re. Special session at ESANN 2013: Sparsity for interpretation and visualization in inference models

[Lisboa, Paulo]

Dear colleague,

The ESANN deadline is fast approaching, now just one month away.  We hope that you are interested in submitting to this special session, in which case this will be indicated as an option on the submission page.  Please do ensure that your paper is submitted by the deadline of 30 November.

With regards,

Paulo Lisboa and Vanya van Belle


Special session proposal at the 21th ESANN
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Bruges (Belgium), 24 - 26 April 2013

www.esann.org<http://www.esann.org/>
Sparsity for interpretation and visualization in inference models
Vanya Van Belle, (1,2), Paulo Lisboa, (2), (1) KU Leuven (Belgium), (2) Liverpool John Moores University (UK)
Machine learning methods have proved to be very flexible and generalizing methods for classification and regression purposes.  A drawback of these methods is their intrinsic black-box nature resulting in a lack of transparency of the predictions and decisions.  In order to extend the use of artificial neural networks and machine learning methods to application domains where visualization and interpretation of decision models are considered to be superior to maximal performance, adaptations to enable the derivation of insights into the decision process are necessary.  This special session focuses on the use of sparsity mechanisms to improve interpretability, transparency and visualization of methods.  Contributions involve, but or not restricted to, the following topics:

-                     Feature selection limited to interpretation and visualization
-                     Sparsity in dual space leading to interpretability
-                     Inference with prototypes
-                     Compact representation of models, e.g. nomograms
-                     Data visualization
-                     p-norm regularization suitable for interpretation
-                     Low rank approximations suitable for interpretation
-                     Applications to real-world data

Deadline

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Prospective authors are invited to submit their contributions before 30 November 2012.

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