Add to ORKGTo accelerate the training of kernel machines, we propose to map the input data to a randomized low-dimensional feature space and then apply existing fast linear methods. The features are designed so that the inner products of the transformed data are approximately equal to those in the feature space of a user specified shiftinvariant kernel. We explore two sets of random features, provide convergence bounds on their ability to approximate various radial basis kernels, and show that in large-scale classification and regression tasks linear machine learning algorithms applied to these features outperform state-of-the-art large-scale kernel machines.
Random Fourier features are a powerful framework to approximate shift invariant kernels with Monte C...
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to...
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
Traditional machine learning has been largely concerned with developing techniques for small or mode...
Kernel methods and neural networks are two important schemes in the supervised learning field. The t...
Approximations based on random Fourier features have recently emerged as an efficient and formally c...
Kernel approximation using random feature maps has recently gained a lot of interest. This is mainly...
Learning a computationally efficient kernel from data is an important machine learning problem. The ...
In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the m...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Kernel methods are nonparametric feature extraction techniques that attempt to boost the learning ca...
Random feature maps are a promising tool for large-scale kernel methods. Since most random feature m...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Random Fourier features are a powerful framework to approximate shift invariant kernels with Monte C...
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to...
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
Traditional machine learning has been largely concerned with developing techniques for small or mode...
Kernel methods and neural networks are two important schemes in the supervised learning field. The t...
Approximations based on random Fourier features have recently emerged as an efficient and formally c...
Kernel approximation using random feature maps has recently gained a lot of interest. This is mainly...
Learning a computationally efficient kernel from data is an important machine learning problem. The ...
In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the m...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Kernel methods are nonparametric feature extraction techniques that attempt to boost the learning ca...
Random feature maps are a promising tool for large-scale kernel methods. Since most random feature m...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Random Fourier features are a powerful framework to approximate shift invariant kernels with Monte C...
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to...
Kernel methods are powerful and flexible approach to solve many problems in machine learning. Due to...