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change remaining c-api CV_ prefixes in constants

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berak
2014-10-04 14:10:12 +02:00
parent b8457be394
commit 7cb3f3def7
49 changed files with 205 additions and 205 deletions
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12
doc/tutorials/ml/introduction_to_svm/introduction_to_svm.rst
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@@ -114,16 +114,16 @@ Explanation
.. code-block:: cpp
CvSVMParams params;
params.svm_type = CvSVM::C_SVC;
params.kernel_type = CvSVM::LINEAR;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 100, 1e-6);
ml::SVM::Params params;
params.svmType = ml::SVM::C_SVC;
params.kernelType = ml::SVM::LINEAR;
params.termCrit = TermCriteria(TermCriteria::MAX_ITER, 100, 1e-6);
* *Type of SVM*. We choose here the type **CvSVM::C_SVC** that can be used for n-class classification (n :math:`\geq` 2). This parameter is defined in the attribute *CvSVMParams.svm_type*.
* *Type of SVM*. We choose here the type **ml::SVM::C_SVC** that can be used for n-class classification (n :math:`\geq` 2). This parameter is defined in the attribute *ml::SVM::Params.svmType*.
.. note:: The important feature of the type of SVM **CvSVM::C_SVC** deals with imperfect separation of classes (i.e. when the training data is non-linearly separable). This feature is not important here since the data is linearly separable and we chose this SVM type only for being the most commonly used.
* *Type of SVM kernel*. We have not talked about kernel functions since they are not interesting for the training data we are dealing with. Nevertheless, let's explain briefly now the main idea behind a kernel function. It is a mapping done to the training data to improve its resemblance to a linearly separable set of data. This mapping consists of increasing the dimensionality of the data and is done efficiently using a kernel function. We choose here the type **CvSVM::LINEAR** which means that no mapping is done. This parameter is defined in the attribute *CvSVMParams.kernel_type*.
* *Type of SVM kernel*. We have not talked about kernel functions since they are not interesting for the training data we are dealing with. Nevertheless, let's explain briefly now the main idea behind a kernel function. It is a mapping done to the training data to improve its resemblance to a linearly separable set of data. This mapping consists of increasing the dimensionality of the data and is done efficiently using a kernel function. We choose here the type **ml::SVM::LINEAR** which means that no mapping is done. This parameter is defined in the attribute *ml::SVMParams.kernel_type*.
* *Termination criteria of the algorithm*. The SVM training procedure is implemented solving a constrained quadratic optimization problem in an **iterative** fashion. Here we specify a maximum number of iterations and a tolerance error so we allow the algorithm to finish in less number of steps even if the optimal hyperplane has not been computed yet. This parameter is defined in a structure :oldbasicstructures:`cvTermCriteria <cvtermcriteria>`.