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fixed SVM train_auto docs: "train set" <-> "test set"

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Alexander Mordvintsev 2012-06-18 11:38:47 +00:00
parent 1736cc9739
commit b4dafa6b58
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modules/ml/doc/support_vector_machines.rst
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@ -198,7 +198,7 @@ Trains an SVM with optimal parameters.
.. ocv:pyfunction:: cv2.SVM.train_auto(trainData, responses, varIdx, sampleIdx, params[, k_fold[, Cgrid[, gammaGrid[, pGrid[, nuGrid[, coeffGrid[, degreeGrid[, balanced]]]]]]]]) -> retval
:param k_fold: Cross-validation parameter. The training set is divided into ``k_fold`` subsets. One subset is used to train the model, the others form the test set. So, the SVM algorithm is executed ``k_fold`` times.
:param k_fold: Cross-validation parameter. The training set is divided into ``k_fold`` subsets. One subset is used to test the model, the others form the train set. So, the SVM algorithm is executed ``k_fold`` times.
:param \*Grid: Iteration grid for the corresponding SVM parameter.