资源分类
》 资源 》论文》Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement

Rule-Enhanced Penalized Regression by Column Generation using Rectangular Maximum Agreement

2020-03-10 | |  70 |   54 |   0

Abstract

We describe a procedure enhancing L1 -penalized regression by adding dynamically generated rules describing multidimensional “box” sets. Our rule-adding procedure is based on the classical column generation method for highdimensional linear programming. The pricing problem for our column generation procedure reduces to the N P-hard rectangular maximum agreement (RMA) problem of finding a box that best discriminates between two weighted datasets. We solve this problem exactly using a parallel branch-and-bound procedure. The resulting rule-enhanced regression method is computation-intensive, but has promising prediction performance.

上一篇：A Divergence Bound for Hybrids of MCMC and Variational Inference and an Application to Langevin Dynamics and SGVI

下一篇：SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...