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The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems.
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Dennis DeCoste and Bernhard Schölkopf · 2002
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G. E Hinton · 2002
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Multiple kernel learning, conic duality, and the SMO algorithm
Francis R. Bach, Gert R. G. Lanckriet, and Michael I. Jordan · 2004
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Learning the kernel matrix with semidefinite programming
Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, and Michael I. Jordan · 2004
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Core vector machines: Fast SVM training on very large data sets
Ivor W. Tsang, James T. Kwok, and Pak-Ming Cheung · 2005
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A fast learning algorithm for deep belief nets
Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh · 2006
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Large Scale Kernel Machines
Léon Bottou, Olivier Chapelle, Dennis DeCoste, and Jason Weston, editors · 2007
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Ali Rahimi and Benjamin Recht · 2008
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Y. Bengio, D. Schuurmans, J.D. Lafferty, C.K.I. Williams, and A. Culotta, editors · 2009
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Coresets, sparse greedy approximation, and the frank-wolfe algorithm
Kenneth L. Clarkson · 2010
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Random feature maps for dot product kernels
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Acoustic modeling using deep belief networks
Abdel-rahman Mohamed, George Dahl, , and Geoffrey Hinton · 2012
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Advances in Neural Information Processing Systems 25 , 2012
F. Pereira, C.J.C. Burges, L. Bottou, and K. Q. Weinberger, editors · 2012
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Deep learning made easier by linear transformations in perceptrons
T. Raiko, H. Valpola, and Y. LeCun · 2012
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Efficient additive kernels via explicit feature maps
A. Vedaldi and A. Zisserman · 2012
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Representation learning: a review and new perspectives
Yoshua Bengio, Aaron C. Courville, and Pascal Vincent · 2013
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Corinna Cortes, Neil Lawrence, and Kilian Weinberger, editors · 2014
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Kernel methods match deep neural networks on TIMIT
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