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Support Vector Machine Implementations for Classification & Clustering

BMC Bioinformatics, 2006
Background We describe Support Vector Machine (SVM) applications to classification and clustering of channel current data. SVMs are variational-calculus based methods that are constrained to have structural risk minimization (SRM), i.e., they provide ...
Winters-Hilt Stephen, Yelundur Anil, McChesney Charlie, Landry Matthew   +3 more
doaj   +1 more source

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Journal of Algorithms & Computational Technology, 2018
Road traffic safety is essential, therefore in order to predict traffic fatalities effectively and promote the harmonious development of transportation, a traffic fatalities prediction model based on support vector machine is established in this paper ...
Xiaoning Gu, Ting Li, Yonghui Wang, Liu Zhang, Yitian Wang, Jinbao Yao   +5 more
doaj   +1 more source

The Study of Information Security Risk Assessment Based on Support Vector Machine

MATEC Web of Conferences, 2018
This paper establishes an information security evaluation model based on support vector machine by analyzing the factors affecting information security risk assessment.
Xie Haiyan, Wang Ying, Zhang Xianghong
doaj   +1 more source

Security risk prediction technology for power monitoring system under the integration of OT and IT

International Journal for Simulation and Multidisciplinary Design Optimization
As an essential force for economic advancement and social stability, the security of the power system has always been a concern. Therefore, the security risks of power monitoring systems are a research focus.
Zhu Zhennan, Jin Jingquan
doaj   +1 more source

Performance Analysis of Simplification of Hyperplane Support Vector Machine

Dianxin kexue, 2015
Comparing with traditional support vector machine，hyperplane support vector machine （HPSVM）and hyperplane support vector machine for regression（HPSVMR）not only reduce the number of support vectors and calculation time but also have comparable accuracy ...
Hui Cheng, Jinglong Fang, Daquan Wang, Xingqi Wang   +3 more
doaj   +2 more sources

Correction: Retrieving interpretability to support vector machine regression models in dynamic system identification. [PDF]

Front Artif Intell
Pena-Campos J, Patino D, Ocampo-Martinez C, Ramos-Fernández JC, Salas-Brown M, Caicedo A.   +5 more
europepmc   +1 more source

Support vector machine-driven Parkinson's disease identification: a 7-Tesla multidimensional structural MRI approach. [PDF]

NPJ Parkinsons Dis
Xiong Y, Li Z, Yang M, Lu H, Duan C, Wang S, Wang X, Huang J, Li Y, Yin X, Guo Y, Gao Z, Lou X.   +12 more
europepmc   +1 more source

Classifying schizophrenia patients and healthy individuals: Whole brain SPECT functional connectivity using support vector machine classification. [PDF]

Neuroimage Rep
Harikumar A, Wardell J, Keator D, Amen D, Plis SM, Calhoun VD.   +5 more
europepmc   +1 more source

Preference learning models based on fuzzy support vector machine

四川大学学报. 自然科学版
Traditional preference learning models cannot deal with uncertain data， while the fuzzy support vector machine can overcome the influence of uncertain data on learning effects.This paper studies the preference learning models based on the fuzzy support ...
LIAO Huchang, LIAO Zhiqiang
doaj  

Comprehensive evaluation of gas-bearing properties in ultra-deep basement reservoirs based on an optimizable support vector machine. [PDF]

Sci Rep
Huang X, Tang J, Zhao J, Li J, Yin S, Feng S, Li H.   +6 more
europepmc   +1 more source