A heterogeneous ensemble of trees
暂无分享,去创建一个
Wen Xin Cheng | Ponnuthurai N. Suganthan | Xueheng Qiu | Rakesh Katuwal | P. Suganthan | Xueheng Qiu | Wen Xin Cheng | Rakesh Katuwal
[1] Ponnuthurai N. Suganthan,et al. Towards generating random forests via extremely randomized trees , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[2] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[3] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[4] Juan José Rodríguez Diez,et al. Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Tin Kam Ho,et al. The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[6] Ponnuthurai N. Suganthan,et al. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.
[7] D. Opitz,et al. Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..
[8] Derek Partridge,et al. Hybrid ensembles and coincident-failure diversity , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).
[9] Ponnuthurai N. Suganthan,et al. Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine , 2015, IEEE Transactions on Cybernetics.
[10] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[11] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[12] David C. Yen,et al. Predicting stock returns by classifier ensembles , 2011, Appl. Soft Comput..
[13] P. N. Suganthan,et al. An Ensemble of Kernel Ridge Regression for Multi-class Classification , 2017, ICCS.
[14] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[15] Jerome H. Friedman,et al. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.
[16] Pierre Geurts,et al. Extremely randomized trees , 2006, Machine Learning.
[17] Tony R. Martinez,et al. Decision Tree Ensemble: Small Heterogeneous Is Better Than Large Homogeneous , 2008, 2008 Seventh International Conference on Machine Learning and Applications.
[18] Leo Breiman,et al. Bias, Variance , And Arcing Classifiers , 1996 .
[19] Ponnuthurai N. Suganthan,et al. Instance based random forest with rotated feature space , 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).
[20] Antonio Criminisi,et al. Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2012, Found. Trends Comput. Graph. Vis..
[21] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[22] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[23] Narendra Ahuja,et al. Robust Visual Tracking Using Oblique Random Forests , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).