A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of th...

Error-Correcting Output Codes (ECOCs) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclasses technique (sub-ECOC), where by splitting the initial classes of the problem we aim to the creation of larger but easier t...

This paper presents a novel method to solve multi-view face detection problem by Error Correcting Output Codes (ECOC). The motivation is that face patterns can be divided into separated classes across views, and ECOC multi-class method can improve the robustness of multi-view face detection compared with the view-based methods because of its inherent error-tolerant ability. One key issue with E...

Error-Correcting Output Codes (ECOC) is a general framework for combining binary classification in order to address the multi-class categorization problem. In this paper, we include contextual and semantic information in the decoding process of the ECOC framework, defining an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of classes based on features and class ...

Error correcting output codes (ECOC) have been proposed to enhance generalization ability of classifiers. If, instead of discrete error functions, continuous error functions are used, unclassifiable regions of multiclass support vector machines are resolved. In this paper, we discuss minimum operations as well as average operations for error functions of support vector machines and show the equ...

Papers published in this report series are preliminary versions of journal articles and not for quotations. Abstract We show that error correcting output codes (ECOC) can further improve the eeects of error dependent adaptive resampling methods such as arc-lh. In traditional one-inn coding, the distance between two binary class labels is rather small, whereas ECOC are chosen to maximize this di...

1. Because the length of code is much less than the number of words, by casting the problem of predicting word to predicting each bit, we can reduce the size of the problem dramatically. Accordingly, the amount of data needed for training can be reduced. 2. By using error correcting output coding, it is possible that we can get better generalization performance on test data. 3. The distributed ...