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.
- svm
- regression
- classification
- rstats
- r-package
- python
- quantile-regression
- apache-spark
- machine-learning
- expectile-regression
- r
- octave
- ml
- c-plus-plus
- matlab
Scala versions:
2.11
1
version found for
liquidSVM-spark