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\expandafter\ifx\csname natexlab\endcsname\relax\def\natexlab#1{#1}\fi
\expandafter\ifx\csname url\endcsname\relax
  \def\url#1{{\tt #1}}\fi

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\newblock Reducing multiclass to binary: A unifying approach for margin
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\newblock {\em Journal of Machine Learning Research}, 1:\penalty0 113--141,
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\bibitem[Bose and Ray-Chaudhuri(1960)]{ML_BR:1960}
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\newblock A training algorithm for optimal margin classifiers.
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\newblock Stability and generalization.
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\newblock Multicategory classification by support vector machines.
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\newblock {SVMT}orch: Support vector machines for large-scale regression
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\newblock Improved output coding for classification using continuous
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\newblock In {\em Proceedings of the Thirteenth Annual Conference on Neural
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\newblock On the algorithmic implementation of multiclass kernel-basd vector
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\newblock On the learnability and design of output codes for multiclass
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\newblock {\em Machine Learning}, 47\penalty0 (2):\penalty0 201--233, 2002.

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\newblock {\em Neural Computation}, 10\penalty0 (7):\penalty0 1895--1924, 1998.

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\newblock Proximal support vector classifiers.
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\newblock Proximal support vector machine classifiers.
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\newblock Classification by pairwise coupling.
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\newblock A comparison of methods for multi-class support vector machines.
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\newblock Making large-scale {SVM} learning practical.
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\bibitem[Kong and Dietterich(1995)]{ML_KD:1995}
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\newblock Why error-correcting output coding works with decision trees.
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\newblock Multicategory support vector machines.
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\newblock Multicategory support vector machines.
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\newblock Support vector machines and the {B}ayes rule in classification.
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\bibitem[Merz and Murphy(1998)]{ML_MM:1998}
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\newblock {UCI} repository of machine learning databases.
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\newblock Fisher discriminant analysis with kernels.
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\newblock {\em Support Vector Machines: Training and Applications}.
\newblock PhD thesis, Massachusetts Institute of Technology, 1998.

\bibitem[Osuna et~al.(1997)Osuna, Freund, and Girosi]{SVM_OFGb:1997}
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\newblock Training support vector machines: an application to face detection.
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\newblock Large margin {DAG}s for multiclass classification.
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\newblock Networks for approximation and learning.
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\newblock {\em C4.5: Programs for Empirical Learning}.
\newblock Morgan Kaufmann, 1993.

\bibitem[Rifkin(2002)]{ML_Rifkin:2002}
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\newblock {\em Everything Old Is New Again: A Fresh Look at Historical
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\newblock PhD thesis, Massachusetts Institute of Technology, 2002.

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\newblock Ridge regression learning algorithm in dual variables.
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\newblock Boosting the margin: a new explanation for the effectiveness of
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\newblock {\em The Annals of Statistics}, 26\penalty0 (5):\penalty0 1651--1686,
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\bibitem[Schapire and Singer(1999)]{ML_SS:1999}
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\newblock Improved boosting algorithms using confidence-rated predictions.
\newblock {\em Machine Learning}, 37\penalty0 (3):\penalty0 297--336, 1999.

\bibitem[Sch\"{o}nberg(1964)]{ML_Schonberg:1964}
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\newblock Spline functions and the problem of graduation.
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\bibitem[Sejnowski and Rosenberg(1987)]{ML_SR:1987}
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\newblock Parallel networks that learn to pronounce {E}nglish text.
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\newblock {\em Neural Processing Letters}, 9\penalty0 (3):\penalty0 293--300,
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\newblock Multiclass least squares support vector machines.
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\bibitem[Vapnik(1998)]{BK_Vapnik:1998}
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\newblock {\em Statistical Learning Theory}.
\newblock John Wiley and Sons, 1998.

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\newblock Society for Industrial and Applied Mathematics, 1990.

\bibitem[Wahba and Kimeldorf(1971)]{ML_WK:1971}
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\newblock Some results on {T}chebycheffian spline functions.
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\bibitem[Weston and Watkins(1998)]{MC_WW:1998}
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\newblock Multi-class support vector machines.
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\bibitem[Witten and Frank(1999)]{ML_Witten:1999}
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\end{thebibliography}
