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@ -62,8 +62,8 @@ namespace dlib
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- y(i) == -1 or +1
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- y(i) is the class that should be assigned to training example x(i)
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- 0 < nu < maximum_nu(y)
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- kernel_function == a kernel function object type as defined at the top
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of this document.
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- kernel_function == a kernel function object type as defined at the
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top of dlib/svm/kernel_abstract.h
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ensures
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- trains a nu support vector classifier given the training samples in x and
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labels in y. Training is done when the error is less than eps.
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@ -112,8 +112,8 @@ namespace dlib
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- y(i) == -1 or +1
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- y(i) is the class that should be assigned to training example x(i)
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- 0 < nu < maximum_nu(y)
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- kernel_function == a kernel function object type as defined at the top
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of this document.
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- kernel_function == a kernel function object type as defined at the
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top of dlib/svm/kernel_abstract.h
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ensures
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- trains a nu support vector classifier given the training samples in x and
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labels in y. Training is done when the error is less than eps.
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@ -158,8 +158,8 @@ namespace dlib
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- y(i) == -1 or +1
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- y(i) is the class that should be assigned to training example x(i)
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- 0 < nu < maximum_nu(y)
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- kernel_function == a kernel function object type as defined at the top
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of this document.
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- kernel_function == a kernel function object type as defined at the
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top of dlib/svm/kernel_abstract.h
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ensures
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- performs k-fold cross validation by training a nu-svm using the svm_nu_train()
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function. Each fold is tested using the learned decision_function and the
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