Markov Encoding for Detecting Signals in Genomic Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
C++ implementation of neural networks trainer
INES'09 Proceedings of the IEEE 13th international conference on Intelligent Engineering Systems
Incorporating linear discriminant analysis in neural tree for multidimensional splitting
Applied Soft Computing
| Hi-index | 0.00 |
The error backpropagation (EBP) training of a multilayer perceptron (MLP) may require a very large number of training epochs. Although the training time can usually be reduced considerably by adopting an on-line training paradigm, it can still be excessive when large networks have to be trained on lots of data. In this paper, a new on-line training algorithm is presented. It is called equalized EBP (EEBP), and it offers improved accuracy, speed, and robustness against badly scaled inputs. A major characteristic of EEBP is its utilization of weight specific learning rates whose relative magnitudes are derived from a priori computable properties of the network and the training data