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<root>
<key>TrainVectorClassifier-libsvm</key>
<exec>otbcli_TrainVectorClassifier</exec>
<longname>TrainVectorClassifier (libsvm)</longname>
<group>Learning</group>
<description>Train a classifier based on labeled geometries and a list of features to consider.</description>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputVectorDataList">ParameterMultipleInput</parameter_type>
<key>io.vd</key>
<name>Input Vector Data</name>
<description>Input geometries used for training (note : all geometries from the layer will be used)</description>
<datatype />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputFilename">ParameterFile</parameter_type>
<key>io.stats</key>
<name>Input XML image statistics file</name>
<description>XML file containing mean and variance of each feature.</description>
<isFolder />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.confmatout</key>
<name>Output confusion matrix</name>
<description>Output file containing the confusion matrix (.csv format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_OutputFilename">OutputFile</parameter_type>
<key>io.out</key>
<name>Output model</name>
<description>Output file containing the model estimated (.txt format).</description>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_StringList">ParameterString</parameter_type>
<key>feat</key>
<name>Field names for training features.</name>
<description>List of field names in the input vector data to be used as features for training.</description>
<options />
<default />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_String">ParameterString</parameter_type>
<key>cfield</key>
<name>Field containing the class id for supervision</name>
<description>Field containing the class id for supervision. Only geometries with this field available will be taken into account.</description>
<default>class</default>
<multiline />
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>layer</key>
<name>Layer Index</name>
<description>Index of the layer to use in the input vector file.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_InputVectorDataList">ParameterMultipleInput</parameter_type>
<key>valid.vd</key>
<name>Validation Vector Data</name>
<description>Geometries used for validation (must contain the same fields used for training, all geometries from the layer will be used)</description>
<datatype />
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>valid.layer</key>
<name>Layer Index</name>
<description>Index of the layer to use in the validation vector file.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier</key>
<name>Classifier to use for the training</name>
<description>Choice of the classifier to use for the training.</description>
<options>
<choices>
<choice>libsvm</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.libsvm.k</key>
<name>SVM Kernel Type</name>
<description>SVM Kernel Type.</description>
<options>
<choices>
<choice>linear</choice>
<choice>rbf</choice>
<choice>poly</choice>
<choice>sigmoid</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Choice">ParameterSelection</parameter_type>
<key>classifier.libsvm.m</key>
<name>SVM Model Type</name>
<description>Type of SVM formulation.</description>
<options>
<choices>
<choice>csvc</choice>
<choice>nusvc</choice>
<choice>oneclass</choice>
</choices>
</options>
<default>0</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Float">ParameterNumber</parameter_type>
<key>classifier.libsvm.c</key>
<name>Cost parameter C</name>
<description>SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.</description>
<minValue />
<maxValue />
<default>1</default>
<optional>False</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
<key>classifier.libsvm.opt</key>
<name>Parameters optimization</name>
<description>SVM parameters optimization flag.</description>
<default>True</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Empty">ParameterBoolean</parameter_type>
<key>classifier.libsvm.prob</key>
<name>Probability estimation</name>
<description>Probability estimation flag.</description>
<default>True</default>
<optional>True</optional>
</parameter>
<parameter>
<parameter_type source_parameter_type="ParameterType_Int">ParameterNumber</parameter_type>
<key>rand</key>
<name>set user defined seed</name>
<description>Set specific seed. with integer value.</description>
<minValue />
<maxValue />
<default>0</default>
<optional>True</optional>
</parameter>
</root>
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