Evolving a cooperative population of neural networks by minimizing mutual information

Proceedings of the IEEE Conference on Evolutionary Computation, ICEC

Volumn 1, Issue , 2001, Pages 384-389

  Liu, Y ^a   Yao, X ^a   Zhao, Q ^a   Higuchi, T ^a  

^a UNIVERSITY OF AIZU   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CORRELATION METHODS; LEARNING SYSTEMS; OPTIMIZATION;

EVOLUTIONARY ENSEMBLES WITH NEGATIVE CORRELATION LEARNING (EENCL);

NEURAL NETWORKS;

EID: 0034863476     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Evolutionary ensembles with negative correlation learning
2. A new evolutionary system for evolving artificial neural networks
3. Making use of population information in evolutionary artificial neural networks
4. Negatively correlated neural networks can produce best ensembles
5. A cooperative ensemble learning system
6. Simultaneous training of negatively correlated neural networks in an ensemble
7. How to make best use of evolutionary learning
8. Towards designing neural network ensembles by evolution
9. Learning internal representations by error propagation
10. Limits for the precision and value of information from dependent sources
11. Cross-validatory choice and assessment of statistical predictions
12. Methods for combining experts' probability assessments