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Corrigendum to “Ensembling neural networks: Many could be better than all” [Artificial Intelligence 137 (1–2) (2002) 239–263]
Artificial Intelligence 174 (2010) 1570
Contents lists available at ScienceDirect
Artificial Intelligence www.elsevier.com/locate/artint
Corrigendum
Corrigendum to “Ensembling neural networks: Many could be better than all” [Artificial Intelligence 137 (1–2) (2002) 239–263] Zhi-Hua Zhou ∗ , Jianxin Wu, Wei Tang National Laboratory for Novel Software Technology, Nanjing University, Hankou Road 22, Nanjing 210093, PR China
In 2002, we published in Artificial Intelligence an extension [1] of a paper we presented at IJCAI-01 [2]. In Section 2 of the IJCAI-01 paper [2] and in Section 2.1 of the AIJ paper [1], we presented a criterion for selecting a subset of an ensemble of neural networks that could yield better performance than using all members of the ensemble for regression. The fundamental motivation for this criterion and its supporting details were first presented in [3]. Although we cited [3] on p. 240 of our article [1], we failed to do so as the source for Section 2.1 and Eqs. (29)–(32) in Section 3, for which we apologize. The main contributions of our paper—the subset search strategy (GASEN) introduced in Section 3 after Eqs. (29)– (32), the extension of the criterion to classification in Section 2.2, and the empirical analysis in Sections 4 and 5—are original. This clarification is the culmination of a thorough review of the papers [1–3] by the members of the AIJ Editorial Board and an expert external reviewer, and has been approved by the AIJ Editors-in-Chief. References [1] Zhi-Hua Zhou, Jian-Xin Wu, Wei Tang, Ensembling neural networks: Many could be better than all, Artificial Intelligence 137 (1–2) (May 2002) 239–263. [2] Zhi-Hua Zhou, Jian-Xin Wu, Yuan Jiang, Shi-Fu Chen, Genetic algorithm based selective neural network ensemble, in: Proceedings of 17th International Joint Conference on Artificial Intelligence, vol. 2, 2001, pp. 797–802. [3] M.P. Perrone, L.N. Cooper, When networks disagree: Ensemble method for neural networks, in: R.J. Mammone (Ed.), Artificial Neural Networks for Speech and Vision, Chapman & Hall, New York, 1993, pp. 126–142.
*
DOI of original article: 10.1016/S0004-3702(02)00190-X. Corresponding author. E-mail address: [email protected] (Z.-H. Zhou).
0004-3702/$ – see front matter doi:10.1016/j.artint.2010.10.001
© 2002 Elsevier B.V.
All rights reserved.
Contents lists available at ScienceDirect
Artificial Intelligence www.elsevier.com/locate/artint
Corrigendum
Corrigendum to “Ensembling neural networks: Many could be better than all” [Artificial Intelligence 137 (1–2) (2002) 239–263] Zhi-Hua Zhou ∗ , Jianxin Wu, Wei Tang National Laboratory for Novel Software Technology, Nanjing University, Hankou Road 22, Nanjing 210093, PR China
In 2002, we published in Artificial Intelligence an extension [1] of a paper we presented at IJCAI-01 [2]. In Section 2 of the IJCAI-01 paper [2] and in Section 2.1 of the AIJ paper [1], we presented a criterion for selecting a subset of an ensemble of neural networks that could yield better performance than using all members of the ensemble for regression. The fundamental motivation for this criterion and its supporting details were first presented in [3]. Although we cited [3] on p. 240 of our article [1], we failed to do so as the source for Section 2.1 and Eqs. (29)–(32) in Section 3, for which we apologize. The main contributions of our paper—the subset search strategy (GASEN) introduced in Section 3 after Eqs. (29)– (32), the extension of the criterion to classification in Section 2.2, and the empirical analysis in Sections 4 and 5—are original. This clarification is the culmination of a thorough review of the papers [1–3] by the members of the AIJ Editorial Board and an expert external reviewer, and has been approved by the AIJ Editors-in-Chief. References [1] Zhi-Hua Zhou, Jian-Xin Wu, Wei Tang, Ensembling neural networks: Many could be better than all, Artificial Intelligence 137 (1–2) (May 2002) 239–263. [2] Zhi-Hua Zhou, Jian-Xin Wu, Yuan Jiang, Shi-Fu Chen, Genetic algorithm based selective neural network ensemble, in: Proceedings of 17th International Joint Conference on Artificial Intelligence, vol. 2, 2001, pp. 797–802. [3] M.P. Perrone, L.N. Cooper, When networks disagree: Ensemble method for neural networks, in: R.J. Mammone (Ed.), Artificial Neural Networks for Speech and Vision, Chapman & Hall, New York, 1993, pp. 126–142.
*
DOI of original article: 10.1016/S0004-3702(02)00190-X. Corresponding author. E-mail address: [email protected] (Z.-H. Zhou).
0004-3702/$ – see front matter doi:10.1016/j.artint.2010.10.001
© 2002 Elsevier B.V.
All rights reserved.