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Nonparametric Classification with Polynomial MPMC Cascades Sander M. Bohte Department of Software Engineering, CWI, The Netherlands Department of Computer Science, University of Colorado at Boulder, USA
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Document Date: 2004-05-10 04:58:54

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Neural Systems / Neural Information Processing Systems / Neural Networks / MIT Press / /

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University of Colorado / /

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cascade-correlation type algorithms / nonparametric algorithms / recent algorithm / nonparametric binary classification algorithms / growth algorithm / dot-product / feedforward network / possible solutions / basis function learning algorithm / learning algorithms / /

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Random Forests / Random forest / Adaboost forest / /

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North Atlantic Treaty Organization / USA Gregory Z. Grudic Department of Computer Science / Polynomial MPMC Cascades Sander M. Bohte Department of Software Engineering / Department of Computer Science / Organization for Scientific Research / MIT / University of Colorado / /

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Gregory Z. Grudic / Morgan Kaufmann / /

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COLT / /

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L / R / ML / /

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Wisconsin / Colorado / /

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Machine Learning / Journal of Machine Learning Research / /

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majority algorithm / proposed PMC algorithms / cascading algorithm / Polynomial MPMC Cascade algorithm / artificial intelligence / Polynomial Cascade algorithm / SVM algorithms / nonparametric Polynomial Cascade Regression Algorithm / nonparametric Polynomial Cascade algorithms / basis function learning algorithm / proposed Polynomial MPMC Cascade algorithms / Polynomial Cascading algorithm / PMC algorithms / recent algorithm / http / Machine Learning / MPMC The Minimax Probability Machine Classification algorithm / Perceptron-based learning algorithms / Polynomial MPCM Cascade Classifier algorithms / nonparametric binary classification algorithms / cascade-correlation type algorithms / PMC algorithm / growth algorithm / MPMC algorithm / /

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