Kernel matrices are crucial in many learning tasks such as support vector machines or kernel ridge regression. The kernel matrix is typically dense and large-scale. Depending on the dimension of the feature space even the computation of all of its entries in reasonable time becomes a challenging task. For such dense matrices the cost of a matrix-vector product scales quadratically with the dimensionality N , if no customized methods are applied. We propose the use of an ANOVA kernel, where we construct several kernels based on lower-dimensional feature spaces for which we provide fast algorithms realizing the matrix-vector products. We employ the non-equispaced fast Fourier transform (NFFT), which is of linear complexity for fixed accuracy....
Abstract We introduce scalable deep kernels, which combine the structural properties of deep learnin...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its conv...
There has been a recent revolution in machine learning based on the following simple idea. Instead o...
In the era of big data, it is highly desired to develop efficient machine learning algorithms to tac...
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for lea...
Huge data sets containing millions of training examples with a large number of attributes (tall fat ...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
169 pagesKernel functions are used in a variety of scientific settings to measure relationships or i...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...
With an immense growth in data, there is a great need for training and testing machine learning mode...
This paper studies an intriguing phenomenon related to the good generalization performance of estima...
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far coul...
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dim...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
Abstract We introduce scalable deep kernels, which combine the structural properties of deep learnin...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its conv...
There has been a recent revolution in machine learning based on the following simple idea. Instead o...
In the era of big data, it is highly desired to develop efficient machine learning algorithms to tac...
In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for lea...
Huge data sets containing millions of training examples with a large number of attributes (tall fat ...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
169 pagesKernel functions are used in a variety of scientific settings to measure relationships or i...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...
With an immense growth in data, there is a great need for training and testing machine learning mode...
This paper studies an intriguing phenomenon related to the good generalization performance of estima...
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far coul...
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dim...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
Abstract We introduce scalable deep kernels, which combine the structural properties of deep learnin...
Although kernel methods efficiently use feature combinations without computing them directly, they d...
Support Vector Machine (SVM) is one of the most powerful machine learning algorithms due to its conv...