A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning
暂无分享,去创建一个
Michael H. Bowling | András György | Csaba Szepesvári | Arash Afkanpour | Csaba Szepesvari | Arash Afkanpour | A. György | Michael Bowling
[1] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[2] Aldric L. Brown,et al. Elements of Functional Analysis , 2014 .
[3] R. Rockafellar. Monotone Operators and the Proximal Point Algorithm , 1976 .
[4] B. Martinet. Perturbation des méthodes d'optimisation. Applications , 1978 .
[5] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[6] Marc Teboulle,et al. Mirror descent and nonlinear projected subgradient methods for convex optimization , 2003, Oper. Res. Lett..
[7] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[8] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[9] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[10] Charles A. Micchelli,et al. Learning the Kernel Function via Regularization , 2005, J. Mach. Learn. Res..
[11] J. Franklin,et al. The elements of statistical learning: data mining, inference and prediction , 2005 .
[12] Charles A. Micchelli,et al. Learning Convex Combinations of Continuously Parameterized Basic Kernels , 2005, COLT.
[13] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[14] Gábor Lugosi,et al. Prediction, learning, and games , 2006 .
[15] Charles A. Micchelli,et al. A DC-programming algorithm for kernel selection , 2006, ICML.
[16] Gunnar Rätsch,et al. Large Scale Multiple Kernel Learning , 2006, J. Mach. Learn. Res..
[17] Elad Hazan,et al. Logarithmic regret algorithms for online convex optimization , 2006, Machine Learning.
[18] Francis R. Bach,et al. Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning , 2008, NIPS.
[19] Sebastian Nowozin,et al. Infinite Kernel Learning , 2008, NIPS 2008.
[20] Zenglin Xu,et al. An Extended Level Method for Efficient Multiple Kernel Learning , 2008, NIPS.
[21] K. R. Ramakrishnan,et al. On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation , 2009, NIPS.
[22] Mehryar Mohri,et al. Learning Non-Linear Combinations of Kernels , 2009, NIPS.
[23] Ambuj Tewari,et al. Stochastic methods for l1 regularized loss minimization , 2009, ICML '09.
[24] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[25] Zenglin Xu,et al. Simple and Efficient Multiple Kernel Learning by Group Lasso , 2010, ICML.
[26] Francesco Orabona,et al. Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning , 2011, ICML.
[27] Elad Hazan,et al. An optimal algorithm for stochastic strongly-convex optimization , 2010, 1006.2425.
[28] Alexander Zien,et al. lp-Norm Multiple Kernel Learning , 2011, J. Mach. Learn. Res..
[29] M. Kloft,et al. l p -Norm Multiple Kernel Learning , 2011 .
[30] Ethem Alpaydin,et al. Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..
[31] Yurii Nesterov,et al. Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..
[32] Michael H. Bowling,et al. A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning , 2012, ICML.
[33] Peter Richtárik,et al. Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function , 2011, Mathematical Programming.
[34] Yurii Nesterov,et al. Subgradient methods for huge-scale optimization problems , 2013, Mathematical Programming.