Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
Random Fourier approximations for skewed multiplicative histogram kernels
Proceedings of the 32nd DAGM conference on Pattern recognition
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Approximations based on random Fourier embeddings have recently emerged as an efficient and formally consistent methodology to design large-scale kernel machines [23]. By expressing the kernel as a Fourier expansion, features are generated based on a finite set of random basis projections, sampled from the Fourier transform of the kernel, with inner products that are Monte Carlo approximations of the original non-linear model. Based on the observation that different kernel-induced Fourier sampling distributions correspond to different kernel parameters, we show that a scalable optimization process in the Fourier domain can be used to identify the different frequency bands that are useful for prediction on training data. This approach allows us to design a family of linear prediction models where we can learn the hyper-parameters of the kernel together with the weights of the feature vectors jointly. Under this methodology, we recover efficient and scalable linear reformulations for both single and multiple kernel learning. Experiments show that our linear models produce fast and accurate predictors for complex datasets such as the Visual Object Challenge 2011 and ImageNet ILSVRC 2011.