Ensemble learning consistently improves the performance of multi-class classification
through aggregating a series of base classifiers. To this end, dataindependent ensemble …

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 …

Existing deep networks are generally initialized with unsupervised methods, such as
random assignments and greedy layerwise pre-training. This may result in the whole …

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 …

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 …

Abstract Error-Correcting Output Codes (ECOC) is widely deployed to tackle the multiclass
classification problem by reducing the original multi-class problem to several binary sub …

We study the problem of scalable design of Error-Correcting Output Codes (ECOC) for multi-
class classification. Prior works on ECOC-based classifiers are limited to codebooks with …

Error-Correcting Output Codes (ECOCs) have been proposed to construct multi-class
classifiers using simple binary classifiers. Recently, the principle of ECOCs has been …

One of the most effective way to address multiclass classification problem is to use a set of
judiciously designed binary classifiers and to carefully combine their results. Error …

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 …