Ensemble learning, online learning and deep learning are very effective and versatile in a
wide spectrum of problem domains, such as feature extraction, multi-class classification and …

N-ary error correcting output codes (ECOC) decompose a multi-class problem into simpler
multi-class problems by splitting the classes into N subsets (meta-classes) to form an …

Abstract Many Error-Correcting Output Codes (ECOC) algorithms had been proposed based
on the hard coding (HC) schemes: binary coding {1, 0} or ternary coding {+ 1,-1, 0}. This …

The technique of error-correcting output codes (ECOC) has been proven to be of high
discriminative ability in many classification applications. However, most algorithms on the …

Error correcting output codes (ECOC) is an effective approach for the multiclass
classification problem by decomposing a multiclass problem to a set of binary class …

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 …

The key to the success of an Error-Correcting Output Code (ECOC) algorithm is the effective
codematrix, which represents a set of class reassignment schemes for decomposing a …

Encoding is one of the most important steps in Error Correcting Output Codes (ECOCs).
Traditional encoding strategies are usually data-independent. Recently, some tree-form …

Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class
classification problems. According to this state of the art technique, a multi-class problem is …

One of the most widely applied techniques to deal with multi-class categorization problems
is the pairwise voting procedure. Recently, this classical approach has been embedded in …