Negative correlation learning and the ambiguity family of ensemble methods

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2709, Issue , 2003, Pages 266-275

  Brown, Gavin ^a   Wyatt, Jeremy ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

AMBIGUITY DECOMPOSITION; DERIVATIVE TECHNIQUE; EMPIRICAL TEST; ENSEMBLE METHODS; ENSEMBLE SYSTEMS; ENSEMBLE TECHNIQUES; FORMALISATION; NEGATIVE CORRELATION LEARNING;

EVOLUTIONARY ALGORITHMS;

EID: 35248848489     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44938-8_27     Document Type: Article

References (12)

1. J. Carney and P. Cunningham. Tuning diversity in bagged neural network ensembles. Technical Report TCD-CS-1999-44, Trinity College Dublin, 1999.
2. Kalyanmoy Deb. Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary Computation, 7(3):205-230, 1999.
3. Vineet Khare and Xin Yao. Artificial speciation of neural network ensembles. In J.A. Bullinaria, editor, Proc. of the 2002 UK Workshop on Computational Intelligence (UKCI'02), pages 96-103. University of Birmingham, UK, September 2002.
4. Anders Krogh and Jesper Vedelsby. Neural network ensembles, cross validation, and active learning. NIPS, 7:231-238, 1995.
5. Yong Liu. Negative Correlation Learning and Evolutionary Neural Network Ensembles. PhD thesis, University College, The University of New South Wales, Australian Defence Force Academy, Canberra, Australia, 1998.
6. Yong Liu and Xin Yao. Negatively correlated neural networks can produce best ensembles. Australian Journal of Intelligent Information Processing Systems, 4(3/4):176-185, 1997.
7. Yong Liu and Xin Yao. Ensemble learning via negative correlation. Neural Networks, 12(10):1399-1404, 1999.
8. David Opitz. Feature selection for ensembles. In Proceedings of 16th National Conference on Artificial Intelligence (AAAI), pages 379-384, 1999.
9. David W. Opitz and Jude W. Shavlik. Generating accurate and diverse members of a neural-network ensemble. NIPS, 8:535-541, 1996.
10. Nikunj C. Oza and Kagan Turner. Input decimation ensembles: Decorrelation through dimensionality reduction. LNCS, 2096:238-247, 2001.
11. Bruce E. Rosen. Ensemble learning using decorrelated neural networks. Connection Science - Special Issue on Combining Artificial Neural Networks: Ensemble Approaches, 8(3 and 4):373-384, 1996.
12. N. Ueda and R. Nakano. Generalization error of ensemble estimators. In Proceedings of International Conference on Neural Networks, pages 90-95, 1996.