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

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

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 …

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 …

Though deep learning has been applied successfully in many scenarios, malicious inputs
with human-imperceptible perturbations can make it vulnerable in real applications. This …

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

Despite their many successful applications, Deep Neural Networks (DNNs) are vulnerable to
intentionally designed adversarial examples. Adversarial robustness describes the ability of …

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 …

One-hot encoding is the prevalent method used in neural networks to represent multi-class
categorical data. Its success stems from its ease of use and interpretability as a probability …

Abstract Error-Correcting Output Codes (ECOCs) offer a principled approach for combining
binary classifiers into multiclass classifiers. In this paper, we study the problem of designing …