Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
Package details |
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Author | Ingo Steinwart, Philipp Thomann |
Maintainer | Philipp Thomann <philipp.thomann@mathematik.uni-stuttgart.de> |
License | AGPL-3 |
Version | 1.2.4 |
URL | https://github.com/liquidSVM/liquidSVM |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
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