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- W3199205344 abstract "New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: the first assumes that all base classifiers are independent and the second assumes that all base classifiers are mutually correlated up to first-order. Moreover, we perform ECOC classification on six datasets and compare their error rates with our bounds to experimentally validate our work and show the effect of correlation on classification accuracy." @default.
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- W3199205344 date "2021-11-01" @default.
- W3199205344 modified "2023-09-27" @default.
- W3199205344 title "Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds" @default.
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- W3199205344 doi "https://doi.org/10.1109/ictai52525.2021.00114" @default.
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