Efficient Cross-validation of the complete Two stages in KFD Classifier Formulation
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This paper presents an efficient evaluation algorithm for cross-validating the two-stage approach of KFD classifiers. The proposed algorithm is of the same complexity level as the existing indirect efficient cross-validation methods but it is more reliable since it is direct and constitutes exact cross-validation for the KFD classifier formulation. Simulations demonstrate that the proposed algorithm is almost as fast as the existing fast indirect evaluation algorithm and the two-stage cross-validation selects better models on most of the thirteen benchmark data sets.
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