A new indirect encoding method with variable length gene code to optimize neural network structures

2003 ◽  
Author(s):  
K. Kobayashi ◽  
M. Ohbayashi
Keyword(s):  
Neural Network ◽  
Variable Length ◽  
Network Structures ◽  
Gene Code ◽  
Indirect Encoding ◽  
Encoding Method

Rapid Discriminative Learning

2004 ◽  
Vol 8 (2) ◽  
pp. 108-114
Author(s):  
Jun Rokui ◽  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Hierarchical Model ◽  
Variable Length ◽  
Discriminative Learning ◽  
Learning Method ◽  
Recognition Method ◽  
Highly Effective ◽  
Hierarchical Neural Network

This paper presents MCE/GPD using GPD that is known as a highly effective discriminative learning method. MCE/GPD is an excellent recognition method that is applicable especially to speech recognition, since it excels in recognizing performance and can be used to deal with variable-length vectors. MCE/GPD involves a problem of calculation resulting from c omplicated algorithms making it impractical. In this paper, we propose a learning method to increase speed at learning based on a hierarchical model. We used a hierarchical neural network to evaluate the method’s performance.

Sign in / Sign up

Export Citation Format

Share Document