Combining Neural Network Voting Classifiers and Error Correcting Output Codes
نویسندگان
چکیده
Papers published in this report series are preliminary versions of journal articles and not for quotations. Abstract We show that error correcting output codes (ECOC) can further improve the eeects of error dependent adaptive resampling methods such as arc-lh. In traditional one-inn coding, the distance between two binary class labels is rather small, whereas ECOC are chosen to maximize this distance. We compare one-inn and ECOC on a multiclass data set using standard MLPs and bagging and arcing voting committees.
منابع مشابه
Combining Neural Network Voting Classiiers and Error Correcting Output Codes
We show that error correcting output codes (ECOC) can further improve the eeects of error dependent adaptive resampling methods such as arc-lh. In traditional one-inn coding, the distance between two binary class labels is rather small, whereas ECOC are chosen to maximize this distance. We compare one-inn and ECOC on a multiclass data set using standard MLPs and bagging and arcing voting commit...
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