Classification of electrocardiogram signals with support vector machines and extreme learning machine

2011 ◽  
Vol 21 (6) ◽  
pp. 1331-1339 ◽  
Author(s):  
S. Karpagachelvi ◽  
M. Arthanari ◽  
M. Sivakumar
Keyword(s):  
Support Vector Machines ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Electrocardiogram Signals ◽  
Vector Machines ◽  
Learning Machine

Fabric Defect Classification Based on Local Region Features and SVM

2012 ◽  
Vol 182-183 ◽  
pp. 634-638
Author(s):  
Yi Hong Li ◽  
Zhao Yang Lu ◽  
Jing Li ◽  
Ling Ling Cui
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Local Region ◽  
Small Samples ◽  
Support Vector ◽  
Defect Classification ◽  
Vector Machines ◽  
Fabric Defect ◽  
Learning Machine

The big differences of the texture and shapes in the same type and certain similarities among heterogeneous types result in the difficult classification of fabric defects. Compared with traditional global statistical method, we put up a new solution, which makes use of the fabric defect local region features to keep the defect property and defect classification by Support Vector Machines (SVM). Based on small-samples learning machine of SVM, we obtain a good performance of less computational load and high recognition rate.

scholarly journals Classification of hyperspectral endocrine tissue images using support vector machines

2020 ◽  
Vol 16 (5) ◽  
pp. 1-10
Author(s):  
Marianne Maktabi ◽  
Hannes Köhler ◽  
Magarita Ivanova ◽  
Thomas Neumuth ◽  
Nada Rayes ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Endocrine Tissue ◽  
Vector Machines

scholarly journals Support vector machines for classification of input vectors with different metrics

2011 ◽  
Vol 61 (9) ◽  
pp. 2874-2878 ◽  
Author(s):  
L. Gonzalez-Abril ◽  
F. Velasco ◽  
J.A. Ortega ◽  
L. Franco
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines
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