The mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. Key features of ThunderSVM are as follows.
- Support all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs.
- Use same command line options as LibSVM.
- Support Python, R and Matlab interfaces.
Why accelerate SVMs: A survey conducted by Kaggle in 2017 shows that 26% of the data mining and machine learning practitioners are users of SVMs.
More information about ThunderSVM
- Getting Started
- Introduction
- Parameters
- How To
- FAQ
- Related Websites
