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D. Opitz and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, pages 169--198, 1999.

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Journal of Machine Learning Research 7 (2006) 1315--1338.. - Yi Zhang Yi-Zhang-   (Correct)

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D. Opitz and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, pages 169--198, 1999.


A New Boosting Algorithm Using Input-Dependent Regularizer - Rong Jin Rong (2003)   (3 citations)  (Correct)

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Opitz, D., & Macline, R. (1999). Popular ensemble methods: An empirical study. Journal of AI Research (pp. 169--198).


Scaling Boosting by Margin-Based Inclusion of Features and.. - Hoche, Wrobel (2002)   (1 citation)  (Correct)

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D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


Effective Rule Induction from Molecular Structures.. - Hoche, Horvath, Wrobel   (Correct)

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D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


Towards Feature Selection for Disk-Based Multirelational.. - Hoche, Wrobel (2003)   (Correct)

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D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


A Comparative Evaluation of Feature Set Evolution Strategies.. - Hoche, Wrobel (2003)   (Correct)

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D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


Ensembles of Similarity-Based Models - Duch, Grudzinski (2001)   (Correct)

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Opitz, D.W., Maclin, R. (1998): Popular ensemble methods: an empirical study, Journal of Artificial Intelligence Research 11, 169-198


Unified Locally Linear Embedding and Linear Discriminant.. - Zhang, Shen, Zhou   (Correct)

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Opitz, D. and Maclin, R. "Popular ensemble methods : an empirical study". J. Art. Intell. Research, 11, 169-198,1999.


Classification of Protein Localisation Patterns via.. - Anastasiadis..   (Correct)

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Opitz D. and Maclin R.: Popular Ensemble Methods: An Empirical Study", Journal of Articial Intelligence Research.11 (1999) 169-198


Combining Machine Learning and Hierarchical Structures for Text.. - Ruiz (2001)   (1 citation)  (Correct)

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D. Opitz and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11:169--198, 1999.


How Weak Text Categorizers Can Strengthen Performance.. - Uren, Addis (2001)   (Correct)

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Opitz, D., & Maclin, R. (1999). "Popular ensemble methods: an empirical study". Journal of Artificial Intelligence Research, Vol.11, pp.169-198.


A Constructive Algorithm for Training Cooperative Neural.. - Islam, Yao, Murase (2003)   (2 citations)  (Correct)

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D. W. Opitz and R. Maclin, "Popular ensemble methods: An empirical study," J. Artificial Intell. Res., vol. 11, pp. 169--198, 1999.


Diversity Creation Methods: A Survey And Categorisation - Brown, Wyatt, Harris, Yao (2004)   (3 citations)  (Correct)

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D. Opitz, R. Maclin, Popular ensemble methods: An empirical study, Journal of Artificial Intelligence Research 11 (1999) 169--198.


Diversity in Neural Network Ensembles - Brown (2003)   (1 citation)  (Correct)

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David Opitz and Richard Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11:169--198, 1999.


Feature Selection and Classifier Ensembles: A Study on.. - Yu (2003)   (Correct)

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D. Opitz and D. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11:169--198, 1999. 12, 30


Scaling Boosting by Margin-Based Inclusion of Features and.. - Hoche, Wrobel (2002)   (1 citation)  (Correct)

No context found.

D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Arti cial Intelligence Research 11, pages 169-198, 1999.


Dynamic Weighted Majority: A New Ensemble Method for Tracking .. - Kolter, Maloof (2003)   (2 citations)  (Correct)

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D. Opitz and R. Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11:169--198, 1999.


Diversity in Neural Network Ensembles - Gavin Brown To (2003)   (1 citation)  (Correct)

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David Opitz and Richard Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11:169--198, 1999.


A Comparative Evaluation of Feature Set Evolution Strategies.. - Hoche, Wrobel (2003)   (Correct)

No context found.

D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


Appendix A - Experimental Details This   (Correct)

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Opitz, D. W., and Maclin, R. Popular ensemble methods: An empirical study. Journal of Arti cial Intelligence Research 11 (1999), 169-198.


Effective Rule Induction from Molecular Structures.. - Hoche, Horvath, Wrobel   (Correct)

No context found.

D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


Towards Feature Selection for Disk-Based Multirelational.. - Hoche, Wrobel (2003)   (Correct)

No context found.

D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.


Constructing Diverse Classifier Ensembles using Artificial.. - Melville, Mooney (2003)   (4 citations)  (Correct)

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David Opitz and Richard Maclin. Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11:169--198, 1999.


Inducing Oblique Decision Trees with Evolutionary Algorithms - Cantu-Paz, Kamath (2003)   (Correct)

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D. Opitz and R. Maclin, "Popular ensemble methods: An empirical study," J. Artif. Intell. Res., vol. 11, pp. 169--198, 1999.


Effective Rule Induction from Molecular Structures.. - Hoche..   (Correct)

No context found.

D. Opitz, and R. Maclin. Popular Ensemble Method: An Empirical Study. Journal of Artificial Intelligence Research 11, pages 169-198, 1999.

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