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One versus all strategy is adopted to select class-specific features for SIP-FS as well as other comparison methods.
Reigns recently stated that it's "one versus all" in WWE, and after this bombshell was made known, it's evident that never have wiser words been uttered.
To overcome this issue, in this paper, we propose a hierarchical multi-class classification method using LAD based on a one versus all (OvA -binary tree, called hierarchical multi-class LAD (HMC-LAD).
However, it is known that most of LAD-based multi-classification algorithms have conflicts between classification accuracy and computational complexity because they are based on class decomposition method such as one versus all or one versus one.
one versus all.
These were error correcting output codes, minimum output codes, one versus one (1vs1) and one versus all.
Similar(43)
Features were used to train four support vector machines offline, in a one-versus-all architecture.
The extension of the traditional problem independent codings one-versus-all and one-versus-one is introduced.
Results reported that the model including RBF kernel which was tuned manually and encoded by one-versus-all scheme provided the highest classification accuracy (93.10%) and consumed 0.98 s for training.
The numerical experiments in this study compare the classification accuracy of subspace ECOC, classical ECOC, one-versus-one, and one-versus-all methods over a set of UCI machine learning repository datasets and two image vision applications.
Ternary Error-Correcting Output Codes (ECOC), which can unify most of the state-of-the-art decomposition frameworks such as one-versus-one, one-versus-all, sparse coding, dense coding, etc., is considered more flexible to model multiclass classification problems than Binary ECOC.
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