Gradient boosting machines, a tutorial
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[1] M Congedo,et al. A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.
[2] Christian Lebiere,et al. The Cascade-Correlation Learning Architecture , 1989, NIPS.
[3] A. Asuncion,et al. UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences , 2007 .
[4] Simon J. Pittman,et al. Multi-Scale Approach for Predicting Fish Species Distributions across Coral Reef Seascapes , 2011, PloS one.
[5] O. Sporns,et al. Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.
[6] Ohad Shamir,et al. Better Mini-Batch Algorithms via Accelerated Gradient Methods , 2011, NIPS.
[7] Patrick van der Smagt,et al. EMG-based teleoperation and manipulation with the DLR LWR-III , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[8] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[9] Trevor Hastie. Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting , 2007 .
[10] Yifan Hu,et al. Efficient, High-Quality Force-Directed Graph Drawing , 2006 .
[11] Oleksandr Makeyev,et al. Neural network with ensembles , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[12] Peter Buhlmann. Boosting for high-dimensional linear models , 2006, math/0606789.
[13] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[14] Wenxin Jiang. On weak base hypotheses and their implications for boosting regression and classification , 2002 .
[15] Torsten Hothorn,et al. Model-based Boosting 2.0 , 2010, J. Mach. Learn. Res..
[16] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[17] Xiaolong Li,et al. Gradient Boosting Learning of Hidden Markov Models , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[18] Ryan M. Rifkin,et al. In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..
[19] Xin Yao,et al. A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..
[20] Thomas G. Dietterich,et al. Training conditional random fields via gradient tree boosting , 2004, ICML.
[21] V Latora,et al. Efficient behavior of small-world networks. , 2001, Physical review letters.
[22] Elias Oliveira,et al. Agglomeration and Elimination of Terms for Dimensionality Reduction , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.
[23] Quoc V. Le,et al. Learning to Rank with Non-Smooth Cost Functions , 2007 .
[24] J Oliver,et al. Earthquake Prediction , 1987, Journal of the World Association for Emergency and Disaster Medicine.
[25] Stefano Soatto,et al. Fast Human Pose Estimation using Appearance and Motion via Multi-Dimensional Boosting Regression , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[26] Edward M. Reingold,et al. Graph drawing by force‐directed placement , 1991, Softw. Pract. Exp..
[27] Huanhuan Chen,et al. Predictive Ensemble Pruning by Expectation Propagation , 2009, IEEE Transactions on Knowledge and Data Engineering.
[28] Bin Yu,et al. Boosting with early stopping: Convergence and consistency , 2005, math/0508276.
[29] Elias Oliveira,et al. An Evolving System Based on Probabilistic Neural Network , 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.
[30] Torsten Hothorn,et al. Geoadditive regression modeling of stream biological condition , 2010, Environmental and Ecological Statistics.
[31] Guanrong Chen,et al. Stability of a neural network model with small-world connections. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.
[32] Tong Zhang,et al. Learning Nonlinear Functions Using Regularized Greedy Forest , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] P. K. Sinha,et al. Pruning of Random Forest classifiers: A survey and future directions , 2012, 2012 International Conference on Data Science & Engineering (ICDSE).
[34] Chang Shu,et al. Artificial neural network ensembles and their application in pooled flood frequency analysis , 2004 .
[35] M S Lewicki,et al. A review of methods for spike sorting: the detection and classification of neural action potentials. , 1998, Network.
[36] D. Simard,et al. Fastest learning in small-world neural networks , 2004, physics/0402076.
[37] Yanjun Qi. Random Forest for Bioinformatics , 2012 .
[38] C. Sutton. Classification and Regression Trees, Bagging, and Boosting , 2005 .
[39] Torsten Hothorn,et al. Flexible boosting of accelerated failure time models , 2008, BMC Bioinformatics.
[40] Nikunj C. Oza,et al. Online Ensemble Learning , 2000, AAAI/IAAI.
[41] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[42] G. De’ath. Boosted trees for ecological modeling and prediction. , 2007, Ecology.
[43] A.R. Runnalls,et al. A Kullback-Leibler Approach to Gaussian Mixture Reduction , 2007 .
[44] Yu Hu,et al. Boosted Mixture Learning of Gaussian Mixture Hidden Markov Models Based on Maximum Likelihood for Speech Recognition , 2011, IEEE Transactions on Audio, Speech, and Language Processing.
[45] Yuan Li,et al. Earthquake Prediction by RBF Neural Network Ensemble , 2004, ISNN.
[46] Luc Van Gool,et al. Random Forests for Real Time 3D Face Analysis , 2012, International Journal of Computer Vision.
[47] R. Shibata. BOOTSTRAP ESTIMATE OF KULLBACK-LEIBLER INFORMATION FOR MODEL SELECTION , 1997 .
[48] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[49] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[50] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[51] Benjamin Hofner,et al. Model-based boosting in R: a hands-on tutorial using the R package mboost , 2012, Computational Statistics.
[52] Thomas G. Dietterich,et al. Incorporating Boosted Regression Trees into Ecological Latent Variable Models , 2011, AAAI.
[53] Stéphan Clémençon,et al. Tree-Based Ranking Methods , 2009, IEEE Transactions on Information Theory.
[54] Torsten Hothorn,et al. Boosting additive models using component-wise P-Splines , 2008, Comput. Stat. Data Anal..