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Volumn 4221 LNCS - I, Issue , 2006, Pages 879-888

Training neural networks using multiobjective Particle Swarm Optimization

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; COMPUTATION THEORY; EVOLUTIONARY ALGORITHMS; OPTIMIZATION; PERSONNEL TRAINING;

EID: 33750341717     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11881070_116     Document Type: Conference Paper
Times cited : (15)

References (23)
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  • 9
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    • Comparison of particle Swarm Optimization and backpropagation as training algorithms for neural networks
    • Indianapolis, IN, USA
    • Gudise V. and Venayagamoorthy G.: Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks. IEEE Swarm Intelligence Symposium, Indianapolis, IN, USA (2003) 110-117
    • (2003) IEEE Swarm Intelligence Symposium , pp. 110-117
    • Gudise, V.1    Venayagamoorthy, G.2
  • 10
    • 0027803368 scopus 로고
    • Keeping neural networks simple by minimizing the description length of the weights
    • Hinton, G. and van Camp, D.: Keeping Neural Networks Simple by Minimizing the Description Length of the Weights. Proceedings of COLT-93 (1993)
    • (1993) Proceedings of COLT-93
    • Hinton, G.1    Van Camp, D.2
  • 11
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    • Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations
    • Third International Conference on Evolutionary Multi-Criterion Optimization Springer, Guanajuato, Mexico
    • Jin, Y., Sendhoff, B. and Körner, E.: Evolutionary Multi-objective Optimization for Simultaneous Generation of Signal-type and Symbol-type Representations. Third International Conference on Evolutionary Multi-Criterion Optimization. LNCS 3410. Springer, Guanajuato, Mexico (2005) 752-766
    • (2005) LNCS , vol.3410 , pp. 752-766
    • Jin, Y.1    Sendhoff, B.2    Körner, E.3
  • 13
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    • A population-based learning algorithm which learns both architectures and weights of neural networks
    • Liu, Y. and Yao, X.: A Population-Based Learning Algorithm Which Learns Both Architectures and Weights of Neural Networks. Chinese J. Advanced Software Res., vol. 3, no. 1 (1996) 54-65
    • (1996) Chinese J. Advanced Software Res. , vol.3 , Issue.1 , pp. 54-65
    • Liu, Y.1    Yao, X.2
  • 16
    • 32444449874 scopus 로고    scopus 로고
    • An effective use of crowding distance in multiobjective Particle Swarm Optimization
    • (Washington DC, USA, June 25-29, 2005). H. Beyer, Ed. GECCO '05. ACM Press, New York, NY
    • Raquel, C. and Naval, P.: An Effective Use of Crowding Distance in Multiobjective Particle Swarm Optimization. Proceedings of the 2005 Conference on Genetic and Evolutionary Computation (Washington DC, USA, June 25-29, 2005). H. Beyer, Ed. GECCO '05. ACM Press, New York, NY (2005) 257-264
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    • An effective search method for neural network based face detection using Particle Swarm Optimization
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    • Sugisaka, M.1    Fan, X.2
  • 19
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    • Particle Swarm weight initialization in multi-layer perceptron artificial neural networks
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    • van den Bergh, F.: Particle Swarm Weight Initialization in Multi-layer Perceptron Artificial Neural Networks. Development and Practice of Artificial Intelligence Techniques (Durban, South Africa) (1999) 41-45
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.