Multiple Kernel Learning with Data Augmentation
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Trung Le | Tu Dinh Nguyen | Dinh Q. Phung | Khanh Nguyen | Vu Nguyen | Vu Nguyen | Khanh-Duy Nguyen | Trung Le | T. Nguyen
[1] Jung-Ying Wang,et al. Application of Support Vector Machines in Bioinformatics , 2002 .
[2] Cheng Soon Ong,et al. Multiclass multiple kernel learning , 2007, ICML '07.
[3] Zhihua Zhang,et al. Bayesian Generalized Kernel Mixed Models , 2011, J. Mach. Learn. Res..
[4] Ning Chen,et al. Dropout training for SVMs with data augmentation , 2015, Frontiers of Computer Science.
[5] Koby Crammer,et al. On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines , 2002, J. Mach. Learn. Res..
[6] Fevzi Alimo. Methods of Combining Multiple Classiiers Based on Diierent Representations for Pen-based Handwritten Digit Recognition , 1996 .
[7] N. Cristianini,et al. On Kernel-Target Alignment , 2001, NIPS.
[8] Andrew Zisserman,et al. A Visual Vocabulary for Flower Classification , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[9] Nicholas G. Polson,et al. Data augmentation for support vector machines , 2011 .
[10] Sham M. Kakade,et al. Mind the Duality Gap: Logarithmic regret algorithms for online optimization , 2008, NIPS.
[11] Klaus-Robert Müller,et al. Efficient and Accurate Lp-Norm Multiple Kernel Learning , 2009, NIPS.
[12] Zenglin Xu,et al. An Extended Level Method for Efficient Multiple Kernel Learning , 2008, NIPS.
[13] Koby Crammer,et al. Kernel Design Using Boosting , 2002, NIPS.
[14] Ethem Alpaydin,et al. Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..
[15] Steve R. Gunn,et al. Result Analysis of the NIPS 2003 Feature Selection Challenge , 2004, NIPS.
[16] Bo Zhang,et al. Fast Parallel SVM using Data Augmentation , 2015, ArXiv.
[17] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[18] S. Chib,et al. Bayesian analysis of binary and polychotomous response data , 1993 .
[19] R. Singer,et al. The Audubon Society field guide to North American mushrooms , 1981 .
[20] Trung Le,et al. Distributed data augmented support vector machine on Spark , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[21] C. Holmes,et al. Bayesian auxiliary variable models for binary and multinomial regression , 2006 .
[22] Leonhard Held,et al. Improved auxiliary mixture sampling for hierarchical models of non-Gaussian data , 2009, Stat. Comput..
[23] Mehmet G nen. Bayesian Efficient Multiple Kernel Learning , 2012, ICML 2012.
[24] William Stafford Noble,et al. Support vector machine , 2013 .
[25] D. F. Andrews,et al. Scale Mixtures of Normal Distributions , 1974 .
[26] Gunnar Rätsch,et al. Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..
[27] Francesco Orabona,et al. Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning , 2011, ICML.
[28] Xiao-Li Meng,et al. Seeking efficient data augmentation schemes via conditional and marginal augmentation , 1999 .
[29] Theodoros Damoulas,et al. Pattern recognition with a Bayesian kernel combination machine , 2009, Pattern Recognit. Lett..
[30] S. V. N. Vishwanathan,et al. Multiple Kernel Learning and the SMO Algorithm , 2010, NIPS.
[31] Barbara Caputo,et al. Online-batch strongly convex Multi Kernel Learning , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[32] Knud D. Andersen,et al. The Mosek Interior Point Optimizer for Linear Programming: An Implementation of the Homogeneous Algorithm , 2000 .
[33] Simon Rogers,et al. Hierarchic Bayesian models for kernel learning , 2005, ICML.
[34] Thomas Hofmann,et al. Data Integration for Classification Problems Employing Gaussian Process Priors , 2007 .
[35] Michael I. Jordan,et al. Multiple kernel learning, conic duality, and the SMO algorithm , 2004, ICML.
[36] Trung Le,et al. One-Pass Logistic Regression for Label-Drift and Large-Scale Classification on Distributed Systems , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).