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293
Classification by pairwise coupling
, 1998
"... We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the BradleyTerry method for paired comparisons. We study the nature of the class probability estim ..."
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Cited by 378 (0 self)
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We discuss a strategy for polychotomous classification that involves estimating class probabilities for each pair of classes, and then coupling the estimates together. The coupling model is similar to the BradleyTerry method for paired comparisons. We study the nature of the class probability
Probability Estimates for Multiclass Classification by Pairwise Coupling
 Journal of Machine Learning Research
, 2003
"... Pairwise coupling is a popular multiclass classification method that combines together all pairwise comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement. ..."
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Cited by 303 (2 self)
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Pairwise coupling is a popular multiclass classification method that combines together all pairwise comparisons for each pair of classes. This paper presents two approaches for obtaining class probabilities. Both methods can be reduced to linear systems and are easy to implement.
Improved Pairwise Coupling Classification with Correcting Classifiers
, 1997
"... The benefits obtained from the decomposition of a classification task involving several classes, into a set of smaller classification problems involving two classes only, usually called dichotomies, have been exposed in various occasions. Among the multiple ways of applying the referred decompositi ..."
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Cited by 33 (3 self)
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decomposition, Pairwise Coupling is one of the best known. Its principle is to separate a pair of classes in each binary subproblem, ignoring the remaining ones, resulting in a decomposition scheme containing as much subproblems as the number of possible pairs of classes in the original task. Pairwise Coupling
Probability Estimates for Multiclass Classification by Pairwise Coupling
"... The two most wellknown approaches for reducing a multiclass classification problem to a set of binary classification problems are known as oneperclass (OPC) and the pairwise coupling (PWC). In the oneperclass approach, we train a classifier for each of the classes using as positive examples the ..."
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The two most wellknown approaches for reducing a multiclass classification problem to a set of binary classification problems are known as oneperclass (OPC) and the pairwise coupling (PWC). In the oneperclass approach, we train a classifier for each of the classes using as positive examples
A.P.: On locally linear classification by pairwise coupling
 In: ICDM. (2008
"... Locally linear classification by pairwise coupling addresses a nonlinear classification problem by three basic phases: decompose the classes of complex concepts into linearly separable subclasses, learn a linear classifier for each pair, and combine pairwise classifiers into a single classifier. A ..."
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Cited by 3 (0 self)
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Locally linear classification by pairwise coupling addresses a nonlinear classification problem by three basic phases: decompose the classes of complex concepts into linearly separable subclasses, learn a linear classifier for each pair, and combine pairwise classifiers into a single classifier. A
Revealing pairwise coupling in linearnonlinear networks
 SIAM Journal on Applied Mathematics
, 2005
"... Abstract. Through an asymptotic analysis of a simple network, we derive an estimate of the coupling between a pair of units when all other units are unobservable. The analysis is based on a model where the response of each unit is a linearnonlinear function of a white noise stimulus. The results ac ..."
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Cited by 18 (1 self)
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Abstract. Through an asymptotic analysis of a simple network, we derive an estimate of the coupling between a pair of units when all other units are unobservable. The analysis is based on a model where the response of each unit is a linearnonlinear function of a white noise stimulus. The results
Pairwise Coupling for Machine Recognition of HandPrinted Japanese Characters
 In CVPR
, 2001
"... Machine recognition of handprinted Japanese characters has been an area of great interest for many years. The major problem with this classification task is the huge number of different characters. Applying standard "stateofthe art" techniques, such as the SVM, to multiclass problems o ..."
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Cited by 12 (1 self)
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is presented that successfully overcomes these shortcomings. It is based on a pairwise coupling procedure for probabilistic twoclass kernel classifiers. Experimental results for Hiragana recognition effectively demonstrate that our method attains an excellent level of prediction accuracy while imposing very
FACE RECOGNITION USING IMPROVED PAIRWISE COUPLING SUPPORT VECTOR MACHINES
"... In this paper, a novel structure is proposed to tackle multiclass classification problem. For a Kclass classification task, an array of K optimal pairwise coupling classifier (OPWC) is constructed, each of which is the most reliable and optimal for the corresponding class in the sense of cross ent ..."
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Cited by 1 (0 self)
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In this paper, a novel structure is proposed to tackle multiclass classification problem. For a Kclass classification task, an array of K optimal pairwise coupling classifier (OPWC) is constructed, each of which is the most reliable and optimal for the corresponding class in the sense of cross
Combining Quadratic Classifier and Pair Discriminators by Pairwise Coupling for Handwritten Chinese Character Recognition
"... The accuracy of handwritten Chinese character recognition can be improved by pair discrimination of similar characters. In this paper, we propose a new method for combining the baseline classifier with incomplete pair discriminators to better exploit their complementariness. The outputs of the basel ..."
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Cited by 2 (0 self)
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of the baseline classifier and pair discriminators are transformed to twoclass probabilities, which are then fused by pairwise coupling (PWC) for final decision. In our experiments using the modified quadratic discriminant function (MQDF) as baseline classifier and LDAbased pair discriminators, the PWC method
Enhancing Fuzzy Rule Based Systems in MultiClassification Using Pairwise Coupling with Preference Relations
"... This contribution proposes a technique for Fuzzy Rule Based Classification Systems (FRBCSs) based on a multiclassifier approach using fuzzy preference relations for dealing with multiclass classification. The idea is to decompose the original dataset into binary classification problems using a ..."
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pairwise coupling approach (confronting all pair of classes), and to obtain a fuzzy system for each one of them. Along the inference process, each FRBCS generates an association degree for its two classes, and these values are encoded into a fuzzy preference relation. The final class of the whole FRBCS
Results 1  10
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293