资源分类
》 资源 》论文》Learning Optimally Sparse Support Vector Machines

Learning Optimally Sparse Support Vector Machines

2020-03-02 | |  66 |   43 |   0

Abstract

We show how to train SVMs with an optimal guarantee on the number of support vectors (up to constants), and with sample complexity and training runtime bounds matching the best known for kernel SVM optimization (i.e. without any additional asymptotic cost beyond standard SVM training). Our method is simple to implement and works well in practice.

上一篇：Stable Coactive Learning via Perturbation

下一篇：Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

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

  • Rating-Boosted La...

    The performance of a recommendation system reli...