.png){kind=icon}
返回
Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2019.2893289.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sequential minimal optimization (SMO) is one of the most popular methods for solving a variety of support vector machines (SVMs). The shrinking and caching techniques are commonly used to accelerate SMO. An interesting phenomenon of SMO is that most of the computational time is wasted on the first half of iterations for building a good solution closing to the optimal. However, as we all know, the stochastic subgradient descent (SSGD) method is extremely fast for building a good solution. In this paper, we propose a generalized framework of accelerating SMO through SSGD for a variety of SVMs of binary classification, regression, ordinal regression, and so on. We also provide a deep insight about why SSGD can accelerate SMO. Experimental results on a variety of datasets and learning applications confirm that our method can effectively speed up SMO.
Keyword:
Acceleration
Kernel
Time complexity
Support vector machines
Buildings
Training
Static VAr compensators
Decomposition method
sequential minimal optimization (SMO)
stochastic gradient descent (SGD)
support vector machines (SVMs)
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
暂无机构信息
引用论文
Effect of Chemical Treatments on the Physical Properties of Non-woven Jute/PLA Biocomposites化学处理对非织造黄麻/PLA生物复合材料物理性能的影响.png)
BioResources
OAAI
BioResourcesIF0
OAAI分享收藏
查看更多内容