PRML 2015 - Special Session on Pattern Recognition and Machine Learning

With the rapid development of computer science, pattern recognition, which has its origins in engineering, plays more and more important role in many modern artificial intelligence field, and more and more new approaches of machine learning have been proposed to solve pattern recognition problems. This session provides a forum for researchers to present and discuss the latest research results, to summarize recent advances, to evaluate existing algorithms and methods, and to timely identify and address emerging problems and challenges with regard to pattern recognition and machine learning. We invite the submission of high-quality, original and unpublished papers in this area. Topics for this session include, but are not limited to:
Kernel methods and support vector machines
Neural networks and deep learning
Ensembles learning
Bayesian methods
Local fisher discriminant analysis
Structured prediction model learning
Transfer learning
Massive data learning
Intelligent control and automation
Computer vision
Manuscripts should follow LNCS format with at most 12 pages. All accepted papers will be published by Springer-Verlag in the Lecture Notes in Computer Science (LNCS) series or a recommended SCI-indexed journal.