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In particular, we apply measures based on mutual information to compare the dependence of ECOC multi-layer perceptron (ECOC MLP), made up by a single multi-input multi-output MLP, and ECOC ensembles made up by a set of independent and parallel dichotomizers (ECOC PND).
OAA, OAO, and ECOC can be described by a code matrix M, where each row contains a code word assigned to each class, and each column defines a binary partition of C classes.
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Xiao and Guo [62] also proposed a semi-supervised HTL method for multi-class DA called Subspace Co-Projection with ECOC (SCP-ECOC).
Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC.
To handle the challenge of multi-class tasks, the proposed method utilizes Error-Correcting Output Code (ECOC) [34] schemes, specifically exhaustive ECOC.
In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework.
Recently, Sheng and Tan (2006) reported a prediction accuracy of around 83% by using error correcting out codes (ECOC), SVM and a recursive feature elimination procedure [ 17].
We review a range of ECoC documents, reports and academic publications with two broad foci.
Ubeyli [11] provided another algorithm based on eigenvector methods and multiclass SVMs with the ECOC for the classification of EEG signals.
Previous studies have presented several well-known coding strategies, including one-against-all (OAA), one-against-one (OAO), decision directed acyclic graph (DDAG), and error correcting output codes (ECOC).
In this study, a new method is presented based on ECOC which improves the performance of sparse ECOC by considering the neighborhood of samples.
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