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Title: facial gender recognition using svm matlab code
Page Link: facial gender recognition using svm matlab code -
Posted By:
Created at: Wednesday 02nd of January 2013 03:42:13 PM
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Title: svm kernel model face classification matlab code
Page Link: svm kernel model face classification matlab code -
Posted By:
Created at: Thursday 22nd of November 2012 02:18:04 PM
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i want Support vector machine matlab code. plz help me. ....etc

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Title: svm for text categorization matlab code
Page Link: svm for text categorization matlab code -
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Created at: Tuesday 13th of November 2012 10:20:04 PM
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Title: matlab code for mammogram classifications using svm classifier
Page Link: matlab code for mammogram classifications using svm classifier -
Posted By:
Created at: Friday 09th of November 2012 04:17:21 PM
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Title: code matlab algorithm pso svm
Page Link: code matlab algorithm pso svm -
Posted By:
Created at: Friday 07th of October 2016 01:48:01 PM
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Title: detection of exudates in diabetic retinopathy images using svm matlab code
Page Link: detection of exudates in diabetic retinopathy images using svm matlab code -
Posted By: kumudham
Created at: Friday 05th of October 2012 07:45:39 PM
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Title: matlab code for color image classification using svm
Page Link: matlab code for color image classification using svm -
Posted By:
Created at: Sunday 07th of October 2012 03:03:57 AM
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Title: matlab code for face recognition using svm
Page Link: matlab code for face recognition using svm -
Posted By:
Created at: Monday 17th of December 2012 01:32:59 PM
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Title: ecg signal classification using svm code in matlab
Page Link: ecg signal classification using svm code in matlab -
Posted By:
Created at: Thursday 22nd of November 2012 02:20:08 PM
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Title: Practical Selection of SVM Parameters and Noise Estimation for SVM Regression
Page Link: Practical Selection of SVM Parameters and Noise Estimation for SVM Regression -
Posted By: smart paper boy
Created at: Friday 29th of July 2011 04:54:38 PM
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Abstract
We investigate practical selection of meta-parameters for SVM regression (that is,
ε -insensitive zone and regularization parameter C). The proposed methodology advocates analytic
parameter selection directly from the training data, rather than resampling approaches commonly
used in SVM applications. Good generalization performance of the proposed parameter selection is
demonstrated empirically using several low-dimensional and high-dimensional regression
problems. Further, we point out the importance of Vapnik’s ε -i ....etc

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