Condition assessment of current transformer based on multi-classification support vector machine

2011 ◽  
Author(s):  
Ning Hao ◽  
Zhuo Dong
Keyword(s):  
Support Vector Machine ◽  
Condition Assessment ◽  
Current Transformer ◽  
Support Vector ◽  
Multi Classification

A Multi-Classification Method Based on Gaussian Processes

2012 ◽  
Vol 198-199 ◽  
pp. 1333-1337 ◽  
Author(s):  
San Xi Wei ◽  
Zong Hai Sun
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Gaussian Process ◽  
Gaussian Processes ◽  
Good Accuracy ◽  
Classification Problem ◽  
Decision Time ◽  
Support Vector ◽  
Regression Problem ◽  
Multi Classification

Gaussian processes (GPs) is a very promising technology that has been applied both in the regression problem and the classification problem. In recent years, models based on Gaussian process priors have attracted much attention in the machine learning. Binary (or two-class, C=2) classification using Gaussian process is a very well-developed method. In this paper, a Multi-classification (C>2) method is illustrated, which is based on Binary GPs classification. A good accuracy can be obtained through this method. Meanwhile, a comparison about decision time and accuracy between this method and Support Vector Machine (SVM) is made during the experiments.

scholarly journals Support Vector Machine Based Classification of Current Transformer Saturation Phenomenon

2017 ◽  
Vol 7 (1) ◽  
pp. 25-42 ◽  
Author(s):  
N. G. Chothani ◽  
D. D. Patel ◽  
K. D. Mistry ◽  
◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Current Transformer ◽  
Support Vector ◽  
Saturation Phenomenon

scholarly journals Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1247
Author(s):  
Mingyang Liu ◽  
Jin Yang ◽  
Wei Zheng
Keyword(s):  
Support Vector Machine ◽  
Maximum Entropy ◽  
Leak Detection ◽  
Least Square ◽  
Support Vector ◽  
Maxent Model ◽  
Water Pipelines ◽  
The Impact ◽  
Multi Classification ◽  
Improved Support Vector Machine

Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.

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