Special Issue in Neural Processing Letters

Call for Papers: Neural Processing Letters, Springer

Important Dates

Final manuscript due date: 20 June 2017 Acceptance of papers: 4 June 2017 Submission of revised version: 20 April 2017 First round of review: 20 March 2017 Paper submission: 20 January 2017

Learning Algorithms for Neural Networks

Guest Editors:

Kazushi Ikeda (Nara Institute of Science and Technology, Japan)

Minho Lee (Kyungpook National University, Korea)

Aims and scope:

Neural networks have been revived as an implement method for Artificial Intelligence in recent years. The essence of neural networks is their learning algorithm. Deep neural networks, for example, introduced new learning techniques such as pre-training and drop-out to improve their performance drastically.

The aim of this special issue is to bring latest development of learning algorithms for neural networks, including both theoretical and application studies. The issue is associated with the 23rd International Conference on Neural Information Processing (ICONIP 2016) and the authors of the papers in ICONIP 2016 are strongly encouraged to submit their extended versions.

Topics: (not limited to)

Deep neural networks Computational intelligence Pattern recognition and computer vision Speech processing and time series analysis Reinforcement learning and robotics control Big data analysis Bioinformatics and biomedical engineering