Kernel machines are integral part of many learning approaches. We have identified 3 key computations encountered in these algorithms. 1. Weighted summation of kernel function (ex. kernel density estimation) 2. Kernel matrix-vector product in an iterative algorithm (ex. kernel regression) 3. Operations on the kernel matrix (ex. kernel PCA)
In this software, we accelerate each of these on a GPU. See the documentation for more details, and demo to see the speedups.
System requirement: Cuda 2.2+, Visual studio 2008 (if used in Windows)
- Changes to previous version:
Initial Announcement on mloss.org.
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