.png){kind=icon}
Return
Accelerating Sequential Minimal Optimization via Stochastic Subgradient Descent
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2019.2893289.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
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.
Keywords:
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 Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
No organization information available
Cited Papers
ShareSaveShareSaveEffect of Chemical Treatments on the Physical Properties of Non-woven Jute/PLA Biocomposites.png)
BioResources
OAAI
BioResourcesIF0
OAAIShareSaveShareSave
View more