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 …

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

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 …

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

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

New bounds on classification error rates for the error-correcting output code (ECOC)
approach in machine learning are presented. These bounds have exponential decay …

Error-correcting output codes (ECOC) is an ensemble method combining a set of binary
classifiers for multi-class learning problems. However, in traditional ECOC framework, the …

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 …

Deep neural networks have enhanced the performance of decision making systems in many
applications, including image understanding, and further gains can be achieved by …

Error correcting codes~(ECCs) are indispensable for reliable transmission in communication
systems. The recent advancements in deep learning have catalyzed the exploration of ECC …