Add to ORKGWe introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a Mondrian process [Roy and Teh, 2009], and we highlight the connection to Mondrian forests [Lakshminarayanan et al., 2014], where trees are also sampled via a Mondrian process, but fit independently. This link provides a new insight into the relationship between kernel methods and random forests
Se estudian diversas formas de usar las Random Fourier Features y el método Nyström con otros modelo...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Random Fourier features are a powerful framework to approximate shift invariant kernels with Monte C...
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is s...
We introduce the Mondrian kernel, a fast $\textit{random feature}$ approximation to the Laplace kern...
This report is concerned with the Mondrian process and its applications in machine learning. The Mon...
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for c...
We present Random Partition Kernels, a new class of kernels derived by demonstrating a natu-ral conn...
We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural conne...
Kernel methods and neural networks are two important schemes in the supervised learning field. The t...
Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be ...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels...
In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the m...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Se estudian diversas formas de usar las Random Fourier Features y el método Nyström con otros modelo...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Random Fourier features are a powerful framework to approximate shift invariant kernels with Monte C...
We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is s...
We introduce the Mondrian kernel, a fast $\textit{random feature}$ approximation to the Laplace kern...
This report is concerned with the Mondrian process and its applications in machine learning. The Mon...
Ensembles of randomized decision trees, usually referred to as random forests, are widely used for c...
We present Random Partition Kernels, a new class of kernels derived by demonstrating a natu-ral conn...
We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural conne...
Kernel methods and neural networks are two important schemes in the supervised learning field. The t...
Random Forests (RF) is one of the algorithms of choice in many supervised learning applications, be ...
One approach to improving the running time of kernel-based machine learning methods is to build a sm...
We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels...
In order to grapple with the conundrum in the scalability of kernel-based learning algorithms, the m...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Se estudian diversas formas de usar las Random Fourier Features y el método Nyström con otros modelo...
To accelerate the training of kernel machines, we propose to map the input data to a randomized low-...
Random Fourier features are a powerful framework to approximate shift invariant kernels with Monte C...