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For linear SVM, the cost parameter needs to be tuned.
where C>0 is the regularization cost parameter.
Thus, it is the key objective of this analysis to identify a new cost parameter set providing better investment cost estimates than currently available cost parameter sets.
Effectively penalising negative and near-zero cost parameter coefficients, this new overall error function delivers a realistic and well-performing cost parameter set when being minimised.
The RBF kernel widths and default value of the cost parameter were used for SVM.
In fact, the cost parameter trades the balance between over-fitting and under-fitting [12, 14].
The adjustment cost parameter estimates are generally smaller under alternative growth rates.
With larger cost parameter value, CRF tends to over-fit to the given training corpus.
The fixed cost parameter appears as a term subtracted from the value equations (8a) and (8b).
The cost parameter limits over-fitting and the γ parameter affects the RBF-kernel.
Table 6 reports the performance results with the optimal value for the cost parameter.
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