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Multicategory Support Vector Machines, theory, and application to the classification of microarray data and satellite radiance data
 Journal of the American Statistical Association
, 2004
"... Twocategory support vector machines (SVM) have been very popular in the machine learning community for classi � cation problems. Solving multicategory problems by a series of binary classi � ers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We pro ..."
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Cited by 261 (25 self)
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Twocategory support vector machines (SVM) have been very popular in the machine learning community for classi � cation problems. Solving multicategory problems by a series of binary classi � ers is quite common in the SVM paradigm; however, this approach may fail under various circumstances. We
The PASCAL Visual Object Classes (VOC) challenge
, 2009
"... ... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has be ..."
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Cited by 624 (20 self)
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... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. This paper describes the dataset and evaluation procedure. We review the stateoftheart in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
Representable Multicategories
 Advances in Mathematics
, 2000
"... We introduce the notion of representable multicategory , which stands in the same relation to that of monoidal category as bration does to contravariant pseudofunctor (into Cat). We give an abstract reformulation of multicategories as monads in a suitable Kleisli bicategory of spans. We describe ..."
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Cited by 51 (6 self)
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We introduce the notion of representable multicategory , which stands in the same relation to that of monoidal category as bration does to contravariant pseudofunctor (into Cat). We give an abstract reformulation of multicategories as monads in a suitable Kleisli bicategory of spans. We describe
A framework for learning predictive structures from multiple tasks and unlabeled data
 Journal of Machine Learning Research
, 2005
"... One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semisupervised learning. Although a number of such methods ar ..."
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Cited by 440 (3 self)
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are proposed, at the current stage, we still don’t have a complete understanding of their effectiveness. This paper investigates a closely related problem, which leads to a novel approach to semisupervised learning. Specifically we consider learning predictive structures on hypothesis spaces (that is, what
Multicategory Classification by Support Vector Machines
 Computational Optimizations and Applications
, 1999
"... We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how twoclass discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programm ..."
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Cited by 85 (0 self)
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We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how twoclass discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic
NEW MULTICATEGORY BOOSTING ALGORITHMS BASED ON MULTICATEGORY FISHERCONSISTENT LOSSES
 SUBMITTED TO THE ANNALS OF APPLIED STATISTICS
"... Fisherconsistent loss functions play a fundamental role in the construction of successful binary marginbased classifiers. In this paper we establish the Fisherconsistency condition for multicategory classification problems. Our approach uses the margin vector concept which can be regarded as a mu ..."
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Cited by 21 (0 self)
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Fisherconsistent loss functions play a fundamental role in the construction of successful binary marginbased classifiers. In this paper we establish the Fisherconsistency condition for multicategory classification problems. Our approach uses the margin vector concept which can be regarded as a
Multicategory Discrimination via Linear Programming
 OPTIMIZATION METHODS AND SOFTWARE
, 1992
"... A single linear program is proposed for discriminating between the elements of k disjoint point sets in the ndimensional real space R n : When the conical hulls of the k sets are (k \Gamma 1)point disjoint in R n+1 , a kpiece piecewiselinear surface generated by the linear program completely ..."
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Cited by 33 (2 self)
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A single linear program is proposed for discriminating between the elements of k disjoint point sets in the ndimensional real space R n : When the conical hulls of the k sets are (k \Gamma 1)point disjoint in R n+1 , a kpiece piecewiselinear surface generated by the linear program completely separates the k sets. This improves on a previous linear programming approach which required that each set be linearly separable from the remaining k \Gamma 1 sets. When the conical hulls of the k sets are not (k \Gamma 1)point disjoint, the proposed linear program generates an errorminimizing piecewiselinear separator for the k sets. For this case it is shown that the null solution is never a unique solver of the linear program and occurs only under the rather rare condition when the mean of each point set equals the mean of the means of the other k \Gamma 1 sets. This makes the proposed linear computational programming formulation useful for approximately discriminating between k sets...
Cloud Classification of Satellite Radiance Data by Multicategory Support Vector Machines
, 2003
"... Two category Support Vector Machines (SVMs) have become very popular in the machine learning community for classification problems, and have recently been shown to have good optimality properties for classification purposes. Treating multicategory problems as a series of binary problems is common ..."
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Cited by 12 (7 self)
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Two category Support Vector Machines (SVMs) have become very popular in the machine learning community for classification problems, and have recently been shown to have good optimality properties for classification purposes. Treating multicategory problems as a series of binary problems
ClassDependent Features and Multicategory Classification
, 2001
"... The problem of pattern classification is considered for the case of multicategory classification where the number of classes, k, is greater than two. Many classification algorithms are in fact 2class classifiers and are generalised to solve kclass problems. Which classifiers are naturally multic ..."
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Cited by 4 (0 self)
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The problem of pattern classification is considered for the case of multicategory classification where the number of classes, k, is greater than two. Many classification algorithms are in fact 2class classifiers and are generalised to solve kclass problems. Which classifiers are naturally
Results 1  10
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