Determination of Global Minima of Some Common Validation Functions in Support Vector Machine

Tuning of the regularization parameter C is a well-known process in the implementation of a support vector machine (SVM) classifier. Such a tuning process uses an appropriate validation function whose value, evaluated over a validation set, has to be optimized for the determination of the optimal C...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2011-04, Vol.22 (4), p.654-659
Main Authors: YANG, Jian-Bo, ONG, Chong-Jin
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Approximation methods
Artificial Intelligence
Colon
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Data processing. List processing. Character string processing
Error analysis
Exact sciences and technology
Heart
Humans
Inference from stochastic processes
time series analysis
Kernel
Mathematical analysis
Mathematical models
Mathematics
Memory organisation. Data processing
Minima
Model selection
Neural networks
Optimization
Pattern Recognition, Automated
Probability and statistics
Regularization
regularization path
Reproducibility of Results
Sciences and techniques of general use
Sensitivity and Specificity
Software
Statistics
support vector machine
Support vector machines
Tuning
tuning of regularization parameter
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Summary:Tuning of the regularization parameter C is a well-known process in the implementation of a support vector machine (SVM) classifier. Such a tuning process uses an appropriate validation function whose value, evaluated over a validation set, has to be optimized for the determination of the optimal C . Unfortunately, most common validation functions are not smooth functions of C . This brief presents a method for obtaining the global optimal solution of these non-smooth validation functions. The method is guaranteed to find the global optimum and relies on the regularization solution path of SVM over a range of C values. When the solution path is available, the computation needed is minimal.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2011.2106219