Art
J-GLOBAL ID:201902287489385422   Reference number:19A0953756

Optimization of Hyperparameters and Feature Selection for Random Forests and Support Vector Machines by Artificial Bee Colony Algorithm

人工蜂コロニーアルゴリズムによるランダムフォレストとサポートベクトルマシンのハイパーパラメータの最適化と特徴選択
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Material:
Volume: 34  Issue:Page: G-I36_1-11(J-STAGE)  Publication year: 2019 
JST Material Number: U0128A  ISSN: 1346-8030  Document type: Article
Article type: 原著論文  Country of issue: Japan (JPN)  Language: JAPANESE (JA)
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All keywords is available on JDreamIII(charged).
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Neurocomputers  ,  Theory of computation  ,  Artificial intelligence  ,  Other methods of operations research 
Reference (28):
  • [Bosman 17] Bosman, P. A. N.: GECCO '17:Proceedings of the Genetic and Evolutionary Computation Conference, ACM (2017)
  • [Breiman 01] Breiman, L.: Random forests, Machine Learning, Vol.45, No.1, pp. 5-32 (2001)
  • [Cortes 95] Cortes, C. and Vapnik, V.: Support-vector networks, Machine Learning, Vol.20, No.3, pp. 273-297 (1995)
  • [Criminisi 11] Criminisi, A., Shotton, J. and Konukoglu, E.: Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning, Technical Report MSR-TR-2011-114, Microsoft Research (2011)
  • [Eberhart 95] Eberhart, R. and Kennedy, J.: A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, MHS'95, pp.39-43, IEEE (1995)
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