Better Digit Recognition with a Committee of Simple Neural Nets
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Luca Maria Gambardella | Jürgen Schmidhuber | Ueli Meier | Dan C. Ciresan | J. Schmidhuber | D. Ciresan | L. Gambardella | U. Meier
[1] L. Cooper,et al. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .
[2] Adam Krzyżak,et al. Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..
[3] Bruce W. Schmeiser,et al. Improving model accuracy using optimal linear combinations of trained neural networks , 1995, IEEE Trans. Neural Networks.
[4] David J. Field,et al. Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.
[5] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[6] Jiri Matas,et al. On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[7] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[8] Naonori Ueda,et al. Optimal Linear Combination of Neural Networks for Improving Classification Performance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[9] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[10] Ludmila I. Kuncheva,et al. A Theoretical Study on Six Classifier Fusion Strategies , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[11] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[12] Robert P. W. Duin,et al. The combining classifier: to train or not to train? , 2002, Object recognition supported by user interaction for service robots.
[13] Nicol N. Schraudolph,et al. Towards stochastic conjugate gradient methods , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[14] Patrice Y. Simard,et al. Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..
[15] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[16] Leo Breiman,et al. Bagging Predictors , 1996, Machine Learning.
[17] Thomas Serre,et al. Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[18] Yoshua Bengio,et al. Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.
[19] Patrice Y. Simard,et al. Combining Multiple Classifiers for Faster Optical Character Recognition , 2006, Document Analysis Systems.
[20] Marc'Aurelio Ranzato,et al. Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.
[21] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[22] Luca Maria Gambardella,et al. Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.
[23] Johannes Stallkamp,et al. The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.
[24] Luca Maria Gambardella,et al. Convolutional Neural Network Committees for Handwritten Character Classification , 2011, 2011 International Conference on Document Analysis and Recognition.
[25] Jürgen Schmidhuber,et al. A committee of neural networks for traffic sign classification , 2011, The 2011 International Joint Conference on Neural Networks.