Model selection for support vector machines: Advantages and disadvantages of the Machine Learning Theory

2010 ◽  
Author(s):  
Davide Anguita ◽  
Alessandro Ghio ◽  
Noemi Greco ◽  
Luca Oneto ◽  
Sandro Ridella
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Model Selection ◽  
Learning Theory ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Vector Machines ◽  
Selection For

A comprehensive review on the variants of support vector machines

2019 ◽  
Vol 33 (25) ◽  
pp. 1950303 ◽  
Author(s):  
Bagesh Kumar ◽  
O. P. Vyas ◽  
Ranjana Vyas
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Model Selection ◽  
Support Vector ◽  
Automated Extraction ◽  
Future Directions ◽  
Comprehensive Review ◽  
Vector Machines ◽  
Unsupervised Approaches ◽  
Inputs And Outputs

Machine learning (ML) represents the automated extraction of models (or patterns) from data. All ML techniques start with data. These data describe the desired relationship between the ML model inputs and outputs, the latter of which may be implicit for unsupervised approaches. Equivalently, these data encode the requirements we wish to be embodied in our ML model. Thereafter, the model selection comes in action, to select an efficient ML model. In this paper, we have focused on various ML models which are the extensions of the well-known ML model, i.e. Support vector machines (SVMs). The main objective of this paper is to compare the existing ML models with the variants of SVM. Limitations of the existing techniques including the variants of SVM are then drawn. Finally, future directions are presented.

scholarly journals Model selection for support vector machines via uniform design

2007 ◽  
Vol 52 (1) ◽  
pp. 335-346 ◽  
Author(s):  
Chien-Ming Huang ◽  
Yuh-Jye Lee ◽  
Dennis K.J. Lin ◽  
Su-Yun Huang
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Uniform Design ◽  
Support Vector ◽  
Vector Machines ◽  
Selection For
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