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PRAI--ACM, Ei and Scopus 2018 : ACM--2018 the International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2018)--Ei Compendex and Scopus

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Link: http://www.prai.net
 
When Aug 15, 2018 - Aug 17, 2018
Where Union, NJ, USA
Submission Deadline Jun 5, 2018
Categories    intelligent systems   machine learning   robotics   automation
 

Call For Papers

2018 the International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2018) (http://www.prai.net/) will be held in Union, NJ, USA at Kean University during August 15-17th, 2018.

◆Publication:
All accepted papers will be published in International Conference Proceedings Series by ACM, indexed by Ei Compendex and Scopus.
Qualified papers will be recommanded to be published on the International Journal of Machine Learning and Computing (IJMLC, ISSN: 2010-3700), indexed by Ei Inspec and Scopus.

◆Keynote &Plenary Speakers:
Prof. Chingsong Wei from City University of New York, USA, Prof. Mehmet Celenk from Ohio University, USA and other two excellent professors from USA and other countries will address the keynote speeches.

◆Submission Method:
Email the submission to praiconf@foxmail.com;
Or submit via EasyChair: https://easychair.org/conferences/?conf=prai2018

◆Contact:
PRAI 2018 Secretary, Mr. Yutao Zhang
Email: praiconf@foxmail.com
Tell: +1-206-456-6022 (USA) +86-28-86528465 (China)

◆CFP: The PRAI 2018 solicits contributions of abstracts, full papers and posters that address themes and topics of the conference. The topics of interest include, but not limited to the following:

I. Pattern Recognition and Machine Learning

Statistical, syntactic and structural pattern recognition
Machine learning and data mining
Artificial neural networks
Dimensionality reduction and manifold learning
Classification and clustering
Graphical Models for Pattern Recognition
Representation and analysis in pixel/voxel images
Support vector machines and kernel methods
Symbolic learning
Active and ensemble learning
Deep learning
Pattern recognition for big data
Transfer learning
Semi-supervised learning and spectral methods
Model selection
Reinforcement learning and temporal models
Performance Evaluation
...

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