Special Issue in Neural Processing Letters
Call for Papers: Neural Processing Letters, Springer
Learning Algorithms for Neural Networks
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.