.png){kind=icon}
返回
Reweighted stochastic learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.08.123.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent advances in stochastic learning, such as dual averaging schemes for proximal subgradient-based methods and simple but theoretically well-grounded solvers for linear Support Vector Machines (SVMs), revealed an ongoing interest in making these approaches consistent, robust and tailored towards sparsity inducing norms. In this paper we study reweighted schemes for stochastic learning (specifically in the context of classification problems) based on linear SVMs and dual averaging methods with primal-dual iterate updates. All these methods favor properties of a convex and composite optimization objective. The latter consists of a convex regularization term and loss function with Lipschitz continuous sub gradients, e.g. l(1)-norm ball together with hinge loss. Some approaches approximate in a limit the l(0)-type of a penalty. In our analysis we focus on a regret and convergence criteria of such an approximation. We derive our results in terms of a sequence of convex and strongly convex optimization objectives. These objectives are obtained via the smoothing of a generic sub-differential and possibly non-smooth composite function by the global proximal operator. We report an extended evaluation and comparison of the reweighted schemes against different state-of-the-art techniques and solvers for linear SVMs. Our experimental study indicates the usefulness of the proposed methods for obtaining sparser and better solutions. We show that reweighted schemes can outperform state-of-the-art traditional approaches in terms of generalization error as well. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Linear SVMs
Stochastic learning
l(0) penalty
Sparsity
Regularization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏
Facile in situ growth of Fe3O4 nanoparticles on hydroxyapatite nanorods for pH dependent adsorption and controlled release of proteins.png)
RSC Adv.
RSC Adv.IF0
PREAI
分享收藏Automatic facies classification using convolutional neural network for three-dimensional outcrop data: Application to the outcrop of the mass-transport deposit基于卷积神经网络的三维露头数据自动相分类：在质量搬运沉积露头中的应用AAPG BULLETIN
AAPG BULLETINIF2.5
PREAI
分享收藏
查看更多内容