The Error Correcting Output Coding (ECOC) approach to classifier design decomposes a multi-class problem into a set of complementary two-class problems. We show how to apply the ECOC concept to automatic face verification, which is inherently a two-class problem. The output of the binary classifiers defines the ECOC feature space, in which it is easier to separate transformed patterns represent...

Error-correcting output codes (ECOCs) represent classes with a set of output bits, where each bit encodes a binary classiication task corresponding to a unique partition of the classes. Algorithms that use ECOCs learn the function corresponding to each bit, and combine them to generate class predictions. ECOCs can reduce both variance and bias errors for multiclass classiication tasks when the ...

A common way to model multi-class classification problems is by means of Error-Correcting Output Codes (ECOC). One of the main requirements of the ECOC design is that the base classifier is capable of splitting each sub-group of classes from each binary problem. In this paper, we present a novel strategy to model multi-class classification problems using sub-class information in the ECOC framew...

Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. In this paper, we propose a novel technique called ECOC-ONE (Optimal Node Embedding) to improve an initial ECOC configuration defining a strategy to create new dichotomies and improve optimally the performance. The process of searching for new dichotomies is guided by t...

Classification (machine learning): How does one algorithmically classify the though a more effective approach could be using error correcting codes: @(cs/9501101) Solving Multiclass Learning Problems via Error-Correcting Output Codes. to solving machine learning problems can be broadly useful.

We describe a new approach for dealing with hierarchical classification with a large number of classes. We build on Error Correcting Output Codes and propose two algorithms that learn compact, binary, low dimensional class codes from a similarity information between classes. This allows building classification algorithms that performs similarly or better than the standard and performing one-vs-...

This paper focuses on a bias variance decomposition analysis of a local learning algorithm, the nearest neighbor classiier, that has been extended with error correcting output codes. This extended algorithm often considerably reduces the 0-1 (i.e., classiication) error in comparison with nearest neighbor (Ricci & Aha, 1997). The analysis presented here reveals that this performance improvement ...