ANNPR: Artificial Neural Networks in Pattern Recognition

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2017 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
ANNPR 2016 Artificial Neural Networks for Pattern Recognition
Sep 28, 2016 - Sep 30, 2016 Ulm University, Germany May 22, 2016
ANNPR 2014 Artificial Neural Networks in Pattern Recognition
Oct 6, 2014 - Oct 8, 2014 Montreal May 5, 2014
ANNPR 2012 IAPR Workshop on Artificial Neural Networks for Pattern Recognition
Sep 17, 2012 - Sep 19, 2012 Trento, Italy May 15, 2012
ANNPR 2008 3rd International Workshop on Artificial Neural Networks in Pattern Recognition
Jul 2, 2008 - Jul 4, 2008 Paris, France Feb 1, 2008
 
 

Present CFP : 2016

The 7th International Workshop on Artificial Neural Networks in Pattern Recognition will be held at Ulm University, Ulm, Germany.
ANNPR 2016 follows the success of ANNPR 2014 (Montreal, Canada), ANNPR 2012 (Trento, Italy), ANNPR 2010 (Cairo, Egypt) and ANNPR 2008 (Paris, France) (to only name a few). The 7th ANNPR workshop will act as a major forum for international researchers and practitioners working in all areas of neural network- and machine learning-based pattern recognition to present and discuss the latest research, results, and ideas in these areas.

Workshop proceedings will be published in the Springer LNAI series.

Authors of selected papers will be invited to submit an extended version of their articles to a dedicated special issue of the journal Neural Processing Letters, guest-edited by the ANNPR2016 Chairs and titled

"Off the mainstream: advances in neural networks and machine learning for pattern recognition"

Papers are solicited dealing with neural networks, machine learning and pattern recognition which emphasize methodological issues possibly arising in applications.

Methodological issues
– Supervised learning
– Unsupervised learning
– Combination of supervised and unsupervised learning
– Feedforward, recurrent, and competitive neural nets
– Hierarchical modular architectures and hybrid systems
– Combination of neural networks and Hidden Markov models
– Multiple classifier systems and ensemble methods
– Probabilistic graphical models
– Kernel methods
– Deep architectures

Applications in Pattern Recognition
– Image processing and segmentation
– Sensor-fusion and multi-modal processing
– Feature extraction, dimension reduction
– Clustering and vector quantization
– Speech and speaker recognition
– Data, text, and web mining
– Bioinformatics

Paper Submission:
Potential participants should submit a paper describing their work in one of the areas described above. Proceedings will be published as a volume in the Springer LNAI, maximum paper length is 12 pages in LNCS/LNAI format. Instructions for authors, LaTeX templates, etc. are available at the Springer LNCS/LNAI web-site. Submission of a paper constitutes a commitment that, if accepted, one or more authors will attend the workshop.
 

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