نتایج جستجو برای: error correcting output codes ecoc

تعداد نتایج: 499423  

2005
J. Ko E. Kim

The Error-Correcting Output Codes (ECOC) is a representative approach of the binary ensemble classifiers for solving multi-class problems. There have been so many researches on an output coding method built on an ECOC foundation. In this paper, we revisit representative conventional ECOC methods in an overlapped learning viewpoint. For this purpose, we propose new OPC based output coding method...

2000
Rayid Ghani

This paper explores in detail the use of Error Correcting Output Coding (ECOC) for learning text classifiers. We show that the accuracy of a Naive Bayes Classifier over text classification tasks can be significantly improved by taking advantage of the error-correcting properties of the code. We also explore the use of different kinds of codes, namely Error-Correcting Codes, Random Codes, and Do...

2011
Miguel Ángel Bautista Sergio Escalera Xavier Baró Oriol Pujol

Error Correcting Output Codes (ECOC) have demonstrate to be a powerful tool for treating multi-class problems. Nevertheless, predefined ECOC designs may not benefit from Errorcorrecting principles for particular multi-class data. In this paper, we introduce the Separability matrix as a tool to study and enhance designs for ECOC coding.

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه شاهد - دانشکده فنی و مهندسی 1387

abstract biometric access control is an automatic system that intelligently provides the access of special actions to predefined individuals. it may use one or more unique features of humans, like fingerprint, iris, gesture, 2d and 3d face images. 2d face image is one of the important features with useful and reliable information for recognition of individuals and systems based on this ...

1997
Friedrich Leisch Kurt Hornik

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...

1997
Kurt Hornik

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 distance. We compare one-inn and ECOC on a multiclass data set using standard MLPs and bagging and arcing voting commit...

2009
Mehdi Mirza-Mohammadi Francesco Ciompi Sergio Escalera Oriol Pujol Petia Radeva

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 ...

2011
Raymond S. Smith M. Bober Terry Windeatt

We compare experimentally the performance of three approaches to ensemble-based classi cation on general multi-class datasets. These are the methods of random forest, error-correcting output codes (ECOC) and ECOC enhanced by the use of bootstrapping and classseparability weighting (ECOC-BW). These experiments suggest that ECOCBW yields better generalisation performance than either random forest...

2007
Sergio Escalera Oriol Pujol Petia Radeva

Error Correcting Output Codes technique (ECOC) represents a general framework capable to extend any binary classification process to the multi-class case. In this work, we present a novel decoding strategy that takes advantage of the ECOC coding to outperform the up to now existing decoding strategies. The results show that the presented methodology considerably increases the performance of the...

2010
Miguel Angel Bautista Sergio Escalera Xavier Baro Oriol Pujol Petia Radeva Jordi Vitria

In this paper, we propose a Compact design of Error Correcting Output Codes (ECOC) in terms of the number of dichotomizers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best Compact ECOC code configuration. The results over several challenging multi-class Computer Vision problems show comparable and even better results than stateof-the-art EC...

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