scholarly journals Spline function smooth support vector machine for classification

2007 ◽  
Vol 3 (3) ◽  
pp. 529-542 ◽  
Author(s):  
Yubo Yuan ◽  
◽  
Weiguo Fan ◽  
Dongmei Pu ◽  
Keyword(s):  
Support Vector Machine ◽  
Spline Function ◽  
Support Vector ◽  
Function Smooth ◽  
Smooth Support Vector Machine

scholarly journals Analysis of Decision Tree and Smooth Support Vector Machine Methods on Data Mining

2019 ◽  
Vol 1255 ◽  
pp. 012067
Author(s):  
Natalina Br Sitepu ◽  
Sawaluddin ◽  
M Zarlis ◽  
Syahril Efendi ◽  
Hanna Willa Dhany
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Support Vector ◽  
Smooth Support Vector Machine

scholarly journals Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fengkai Liu ◽  
Xingmin Ma ◽  
Xingshuo An ◽  
Guangnan Liang
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Traffic Flow ◽  
Urban Traffic ◽  
Support Vector ◽  
Swarm Optimization ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Chaotic Particle Swarm Optimization ◽  
Smooth Support Vector Machine

Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic flow prediction in a metropolitan area, this paper proposes an urban traffic flow prediction model based on chaotic particle swarm optimization algorithm-smooth support vector machine (CPSO/SSVM). The prediction model has built a new second-order smooth function to achieve better approximation and regression effects and has further improved the computational efficiency of the smooth support vector machine algorithm through chaotic particle swarm optimization. Simulation experiment results show that this model can accurately predict urban traffic flow.

scholarly journals Gender Classification Based on Geometry Features of Palm Image

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ming Wu ◽  
Yubo Yuan
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
The Other ◽  
Classification Method ◽  
Support Vector ◽  
Gender Classification ◽  
Gender Recognition ◽  
Classification Rate ◽  
Classification Approach ◽  
Smooth Support Vector Machine

This paper presents a novel gender classification method based on geometry features of palm image which is simple, fast, and easy to handle. This gender classification method based on geometry features comprises two main attributes. The first one is feature extraction by image processing. The other one is classification system with polynomial smooth support vector machine (PSSVM). A total of 180 palm images were collected from 30 persons to verify the validity of the proposed gender classification approach and the results are satisfactory with classification rate over 85%. Experimental results demonstrate that our proposed approach is feasible and effective in gender recognition.

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