Coupling of evolution and learning to optimize a hierarchical object recognition model

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

Volumn 3242, Issue , 2004, Pages 662-671

  Schneider, Georg ^a   Wersing, Heiko ^a   Sendhoff, Bernhard ^a   Körner, Edgar ^a  

^a HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; NETWORK ARCHITECTURE; NEURAL NETWORKS; OBJECT RECOGNITION; OPTIMIZATION;

BIOLOGICALLY INSPIRED; EVOLUTIONARY OPTIMIZATION METHOD; EVOLUTIONARY OPTIMIZATIONS; HIERARCHICAL OBJECTS; INDIRECT CODING; RECOGNITION MODELS; STATE OF THE ART; VISUAL OBJECT RECOGNITION;

PROBLEM SOLVING;

EID: 35048901563     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30217-9_67     Document Type: Article

References (16)

1. X. Yao, "Evolving artificial neural networks," Proc. IEEE, vol. 87, no, 9, pp. 1423-1447, 1999.
2. M. Riesenhuber and T. Poggio, "Hierarchical models of object recognition in cortex," Nature Neuroscience, vol. 2, no. 11, pp. 1019-1025, 1999.
3. H. B. Barlow, "The twelfth Bartlett memorial lecture: The role of single neurons in the psychology of perception," Quart. J. Exp. Psychol., vol. 37, pp. 121-145, 1985.
4. B. A. Olshausen and D. J. Field, "Sparse coding with an overcomplete basis set: A strategy employed by VI ?" Vision Research, vol. 37, pp. 3311-3325, 1997.
5. H. Kitano, "Designing neural networks using genetic algorithms with graph generation system," Complex Systems, vol. 4, pp. 461-476, 1990.
6. E. T. Rolls and S. M. Stringer, "On the design of neural networks in the brain by genetic evolution," Progress in Neurobiology, vol. 6, no. 61, pp. 557-579, 2000.
7. B. Sendhoff and M. Kreutz, "A model for the dynamic interaction between evolution and learning," Neural Processing Letters, vol. 10, no. 3, pp. 181-193, 1999.
8. S. Quartz and T. Sejnowski, "The neural basis of cognitive development: A constructivist manifesto," Behavioral and Brain Sciences, vol. 9, pp. 537-596, 1997.
9. A. G. Rust, R. Adams, S. George, and H. Bolouri, "Towards computational neural systems through developmental evolution," in LNCS, S. W. et al., Ed., vol. 2036, 2001, pp. 188-202.
10. H. Wersing and E. Körner, "Learning optimized features for hierarchical models of invariant recognition," Neural Computation, vol. 15, no. 7, pp. 1559-1588, 2003.
11. K. Fukushima, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position," Biol. Cyb., vol. 39, pp. 139-202, 1980.
12. H.-P. Schwefel and G. Rudolph, "Contemporary evolution strategies," in Proc. of the Third European Conf. on Artificial Life: Advances in Artificial Life, ser. LNAI, F. M. et al., Ed., vol. 929. Berlin: Springer Verlag, June 1995, pp. 893-907.
13. A. Hyvärinen and E. Oja, "A fast fixed-point algorithm for independent component analysis," Neural Computation, vol. 9, no. 7, pp. 1483-1492, 1997.
14. H. Wersing and E. Körner, "Unsupervised learning of combination features for hierarchical recognition models," in Int. Conf. Artif. New. Netw. ICANN, J. R. D. et al., Ed. Springer, 2002, pp. 1225-1230.
15. S. K. Nayar, S. A. Nene, and H. Murase, "Real-time 100 object recognition system," in Proc. of ARPA Image Understanding Workshop, Palm Springs, 1996.
16. D. Roobaert and M. V. Hulle, "View-based 3d object recognition with support vector machines," in Proc. IEEE Int. Workshop on Neural Networks for Signal Processing, Madison, USA. New York, USA: IEEE, 1999, pp. 77-84.