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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.

CMake BASIS makes it easy to create sharable software and libraries that work together. This is accomplished by combining and documenting some of the best practices and utilities available. This project supplies a fully integrated suite of functionality to make the whole process seamless!

  • cmake
  • project-template
  • project-creation
  • cmake-basis