Topic: "expectile-regression"
liquidSVM/liquidSVM
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.
Language: C++ - Size: 4.36 MB - Last synced at: about 2 months ago - Pushed at: over 5 years ago - Stars: 67 - Forks: 9
getzze/RobustModels.jl
A Julia package for robust regressions using M-estimators and quantile regressions
Language: Julia - Size: 782 KB - Last synced at: 22 days ago - Pushed at: 9 months ago - Stars: 36 - Forks: 2
MarianaVideira/ExpectileEconometrics
Project for Econometrics II Nova SBE PhD
Language: R - Size: 110 KB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 1 - Forks: 0
