It is known that the Error Correcting Output Code (ECOC) technique, when applied to multiclass learning problems, can improve generalisation performance. One reason for the improvement is its ability to decompose the original problem into complementary two-class problems. Binary classifiers trained on the sub-problems are diverse and can benefit from combining using a simple distance-based stra...

We describe a new algorithmic framework for learning multiclass categorization problems. In this framework a multiclass predictor is composed of a pair of embeddings that map both instances and labels into a common space. In this space each instance is assigned the label it is nearest to. We outline and analyze an algorithm, termed Bunching, for learning the pair of embeddings from labeled data...

Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupled into a set of binary problems that are solved independently. However, literature defines a general error-correcting capability for ECOCs without...

This paper applies error-correcting output coding (ECOC) to the task of document categorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classification problem into many binary classification tasks, and then combining the results of the subtasks into a hypothesized solution to the original problem. There has been much recent interest in the machine l...

Error-correcting output codes (ECOC) are a successful technique to combine a set of binary classifiers for multi-class learning problems. However, in traditional ECOC framework, all the base classifiers are trained independently according to the defined ECOC matrix. In this paper, we reformulate the ECOC models from the perspective of multi-task learning, where the binary classifiers are learne...

A new approach based on the implementation of support vector machine (SVM) with the error correcting output codes (ECOC) is presented for recognition of multi-class protein folds. The experimental show that the proposed method can improve prediction accuracy by 4%-10% on two datasets containing 27 SCOP folds.

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 ECOCONE (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 th...

Error-correcting output codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. With the extension of the binary ECOC to the ternary ECOC framework, ECOC designs have been proposed in order to better adapt to distributions of the data. In order to decode ternary matrices, recent works redefined many decoding strategie...

Error-correcting output codes (ECOC) have emerged in machine learning as a successful implementation of the idea of distributed classes. Monadic class symbols are replaced by bit strings, which are learned by an ensemble of binary-valued classifiers (dichotomizers). In this study, the idea of ECOC is applied to memory-based language learning with local (knearest neighbor) classifiers. Regressio...