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On the algorithmic implementation of multiclass kernelbased vector machines
 Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernelbased vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
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Cited by 559 (13 self)
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objective function. Unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks, our notion of margin yields a direct method for training multiclass predictors. By using the dual of the optimization problem we are able
Multiclass learning by probabilistic embeddings
 In Advances in Neural Information Processing Systems 15
, 2002
"... 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 outlin ..."
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Cited by 19 (0 self)
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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
Uncovering shared structures in multiclass classification
 In Proceedings of the Twentyfourth International Conference on Machine Learning
, 2007
"... This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using tracenorm regularization and study g ..."
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Cited by 103 (0 self)
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This paper suggests a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using tracenorm regularization and study
ShareBoost: Efficient Multiclass Learning with Feature Sharing
"... Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sublinearly with the number of possible classes. This imp ..."
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Cited by 4 (0 self)
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Multiclass prediction is the problem of classifying an object into a relevant target class. We consider the problem of learning a multiclass predictor that uses only few features, and in particular, the number of used features should increase sublinearly with the number of possible classes
A Theory of Multiclass Boosting
"... Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the “correct ” requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we ..."
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Cited by 16 (0 self)
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Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the “correct ” requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we
Multiclass Boosting: Theory and Algorithms
"... The problem of multiclass boosting is considered. A new framework, based on multidimensional codewords and predictors is introduced. The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functional s ..."
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Cited by 10 (3 self)
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The problem of multiclass boosting is considered. A new framework, based on multidimensional codewords and predictors is introduced. The optimal set of codewords is derived, and a margin enforcing loss proposed. The resulting risk is minimized by gradient descent on a multidimensional functional
A Theory of Multiclass Boosting A Theory of Multiclass Boosting
"... Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the “correct ” requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we ..."
Abstract
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Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the “correct ” requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we
Multiclass Support Vector Machines with SCAD
"... Classification is an important research field in pattern recognition with highdimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the nonconvex penalty function, and the smoothly clipped absolute deviation( ..."
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Classification is an important research field in pattern recognition with highdimensional predictors. The support vector machine(SVM) is a penalized feature selector and classifier. It is based on the hinge loss function, the nonconvex penalty function, and the smoothly clipped absolute deviation
Uncovering Shared Structures in Multiclass Classification
"... We suggest a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using tracenorm regularization, study gradientbas ..."
Abstract
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We suggest a method for multiclass learning with many classes by simultaneously learning shared characteristics common to the classes, and predictors for the classes in terms of these characteristics. We cast this as a convex optimization problem, using tracenorm regularization, study gradient
MBACT Multiclass Bayesian Additive Classification Trees
, 2014
"... In this article, we propose Multiclass Bayesian Additive Classification Trees (MBACT) as a nonparametric procedure to deal with multiclass classification problems. MBACT is a multiclass extension of BART: Bayesian Additive Regression Trees [Chipman et al., 2010]. In a range of data generating schem ..."
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In this article, we propose Multiclass Bayesian Additive Classification Trees (MBACT) as a nonparametric procedure to deal with multiclass classification problems. MBACT is a multiclass extension of BART: Bayesian Additive Regression Trees [Chipman et al., 2010]. In a range of data generating
Results 1  10
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