Texture classification by support vector machines with kernels for higher-order Gabor filtering

2005 ◽  
Author(s):  
K. Kameyama ◽  
K. Taga
Keyword(s):  
Support Vector Machines ◽  
Texture Classification ◽  
Higher Order ◽  
Support Vector ◽  
Gabor Filtering ◽  
Vector Machines

Texture classification using the support vector machines

2003 ◽  
Vol 36 (12) ◽  
pp. 2883-2893 ◽  
Author(s):  
Shutao Li ◽  
James T. Kwok ◽  
Hailong Zhu ◽  
Yaonan Wang
Keyword(s):  
Support Vector Machines ◽  
Texture Classification ◽  
Support Vector ◽  
Vector Machines

An Unsupervised Method based on Support Vector Machines and Higher-Order Statistics for Mechanical Faults Detection

2020 ◽  
Vol 18 (06) ◽  
pp. 1093-1101 ◽  
Author(s):  
Fernando Borges ◽  
Andrey Pinto ◽  
Diogo Ribeiro ◽  
Tassio Barbosa ◽  
Daniel Pereira ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Order Statistics ◽  
Higher Order ◽  
Support Vector ◽  
Higher Order Statistics ◽  
Unsupervised Method ◽  
Vector Machines ◽  
Mechanical Faults

A Study of the Screening Efficiency of a Probability Sieve Based on Higher-Order Spectrum Analysis and Support Vector Machines

2011 ◽  
Vol 291-294 ◽  
pp. 2089-2093
Author(s):  
Zheng Zhong Shi ◽  
Yi Jian Huang
Keyword(s):  
Support Vector Machines ◽  
Least Square Method ◽  
Higher Order ◽  
Least Square ◽  
Support Vector ◽  
Vibration Signals ◽  
Order Spectrum ◽  
Vector Machines ◽  
Screening Efficiency ◽  
Relative Errors

Aiming at drawbacks of current methods for predicting the screening efficiency of probability sieve, this paper proposed a method of predict and study the screening efficiency of probability sieve based on higher-order spectrum(HOS) analysis and support vector machines(SVMs). First setting up trispectrum model with the vibration signals, then fitting out polynomial with least square method using the data which get out by the reconstruct power spectrum. Finaly, using support vector machines to predicting the screening efficiency with the coefficient of the polynomial as the sample input. The results show that the relative errors are all less than 2.4% and the absolute errors are all less than 0.021, which is ideal for efficiency forecast.

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