Papers › pGMM Kernel Regression and Comparisons with Boosted Trees
pGMM Kernel Regression and Comparisons with Boosted Trees
Ping Li, Weijie Zhao
In this work, we demonstrate the advantage of the pGMM (``powered generalized min-max'') kernel in the context of (ridge) regression. In recent prior studies, the pGMM kernel has been extensively evaluated for classification tasks, for logistic regression, support vector machines, as well as deep neural networks. In this paper, we provide an experimental study on ridge regression, to compare the pGMM kernel regression with the ordinary ridge linear regression as well as the RBF kernel ridge regression. Perhaps surprisingly, even without a tuning parameter (i.e., p=1 for the power parameter of the pGMM kernel), the pGMM kernel already performs well. Furthermore, by tuning the parameter p, this (deceptively simple) pGMM kernel even performs quite comparably to boosted trees. Boosting and boosted trees are very popular in machine learning practice. For regression tasks, typically, practitioners use L₂ boost, i.e., for minimizing the L₂ loss. Sometimes for the purpose of robustness, the L₁ boost might be a choice. In this study, we implement Lₚ boost for p≥1 and include it in the package of ``Fast ABC-Boost''. Perhaps also surprisingly, the best performance (in terms of L₂ regression loss) is often attained at p>2, in some cases at p≫2. This phenomenon has already been demonstrated by Li et al (UAI 2010) in the context of k-nearest neighbor classification using Lₚ distances. In summary, the implementation of Lₚ boost provides practitioners the additional flexibility of tuning boosting algorithms for potentially achieving better accuracy in regression applications.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections