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<h3>In Defense of OneVsAll Classification</h3><p>The resulting classifiers, which we call threshold decision lists act as follows. Some points of the data set to  In this paper we consider the generalization accuracy of classification methods based on the iterative use of linear classifiers. </p>

In Defense of OneVsAll Classification

The resulting classifiers, which we call threshold decision lists act as follows. Some points of the data set to In this paper we consider the generalization accuracy of classification methods based on the iterative use of linear classifiers.

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<h3>Beyond sparsity: The role of L1optimizer in pattern </h3><p>The normalized class L 1optimizer classifiers NCCL 1 C and NLCL 1 C can further improve the performance. The two classifiers consistently outperform the NN classifier and SRC, irrespective of the variation of dimensions. SRC does not perform well on this database, even worse than the NN classifier. </p>

Beyond sparsity: The role of L1optimizer in pattern

The normalized class L 1optimizer classifiers NCCL 1 C and NLCL 1 C can further improve the performance. The two classifiers consistently outperform the NN classifier and SRC, irrespective of the variation of dimensions. SRC does not perform well on this database, even worse than the NN classifier.

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<h3>(PDF) Training Effective Node Classifiers for Cascade </h3><p>Training Effective Node Classifiers for Cascade Classification.  Weak classifiers are vertical and horizontal decision stumps.  by multiplying a matrix A with a v ector w as Aw and. its i th  </p>

(PDF) Training Effective Node Classifiers for Cascade

Training Effective Node Classifiers for Cascade Classification. Weak classifiers are vertical and horizontal decision stumps. by multiplying a matrix A with a v ector w as Aw and. its i th

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<h3>UCI Machine Learning Repository: Ionosphere Data Set</h3><p>Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998. [View Context]. Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [View Context]. Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997. [View Context]. </p>

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Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998. [View Context]. Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [View Context]. Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997. [View Context].

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<h3>(PDF) Training Effective Node Classifiers for Cascade </h3><p>Training Effective Node Classifiers for Cascade Classification.  Weak classifiers are vertical and horizontal decision stumps.  by multiplying a matrix A with a v ector w as Aw and. its i th  </p>3

(PDF) Training Effective Node Classifiers for Cascade

Training Effective Node Classifiers for Cascade Classification. Weak classifiers are vertical and horizontal decision stumps. by multiplying a matrix A with a v ector w as Aw and. its i th

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<h3>UCI Machine Learning Repository: Ionosphere Data Set</h3><p>Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998. [View Context]. Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [View Context]. Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997. [View Context]. </p>

UCI Machine Learning Repository: Ionosphere Data Set

Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS. 1998. [View Context]. Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [View Context]. Kristin P. Bennett and Erin J. Bredensteiner. A Parametric Optimization Method for Machine Learning. INFORMS Journal on Computing, 9. 1997. [View Context].

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<h3>Prioritization of Retinal Disease Genes: An Integrative Approach</h3><p>The conerod homeobox  Nontraditional Classifiers.  Song YQ, Kung AW. Prediction of osteoporosis candidate genes by computational diseasegene identification  </p>

Prioritization of Retinal Disease Genes: An Integrative Approach

The conerod homeobox Nontraditional Classifiers. Song YQ, Kung AW. Prediction of osteoporosis candidate genes by computational diseasegene identification

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<h3>(PDF) Duality and Geometry in SVM Classifiers</h3><p>Duality and Geometry in SVM Classifiers. Article (PDF Available) · September 2000  and  exist suc h that: Aw &gte  </p>

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Duality and Geometry in SVM Classifiers. Article (PDF Available) · September 2000 and exist suc h that: Aw >e

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