TrainRegression-libsvm otbcli_TrainRegression TrainRegression (libsvm) Learning Train a classifier from multiple images to perform regression. ParameterMultipleInput io.il Input Image List A list of input images. First (n-1) bands should contain the predictor. The last band should contain the output value to predict. False ParameterFile io.csv Input CSV file Input CSV file containing the predictors, and the output values in last column. Only used when no input image is given True ParameterFile io.imstat Input XML image statistics file Input XML file containing the mean and the standard deviation of the input images. True OutputFile io.out Output regression model Output file containing the model estimated (.txt format). ParameterNumber io.mse Mean Square Error Mean square error computed with the validation predictors 0.0 False ParameterNumber sample.mt Maximum training predictors Maximum number of training predictors (default = 1000) (no limit = -1). 1000 False ParameterNumber sample.mv Maximum validation predictors Maximum number of validation predictors (default = 1000) (no limit = -1). 1000 False ParameterNumber sample.vtr Training and validation sample ratio Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). 0.5 False ParameterSelection classifier Classifier to use for the training Choice of the classifier to use for the training. libsvm 0 False ParameterSelection classifier.libsvm.k SVM Kernel Type SVM Kernel Type. linear rbf poly sigmoid 0 False ParameterSelection classifier.libsvm.m SVM Model Type Type of SVM formulation. epssvr nusvr 0 False ParameterNumber classifier.libsvm.c Cost parameter C SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. 1 False ParameterBoolean classifier.libsvm.opt Parameters optimization SVM parameters optimization flag. True True ParameterBoolean classifier.libsvm.prob Probability estimation Probability estimation flag. True True ParameterNumber classifier.libsvm.eps Epsilon 0.001 False ParameterNumber classifier.libsvm.nu Nu 0.5 False ParameterNumber rand set user defined seed Set specific seed. with integer value. 0 True