DESCRIPTION

This is the TrainRegression application, version 5.2.0 Train a classifier from multiple images to perform regression.

Complete documentation: http://www.orfeo-toolbox.org/Applications/TrainRegression.html

Parameters:

-progress

<boolean> Report progress

 -io.il                 <string list>    Input Image List  (mandatory)

-io.csv

<string> Input CSV file (optional, off by default)

-io.imstat

<string> Input XML image statistics file (optional, off by default)

 -io.out                <string>         Output regression model  (mandatory)

-sample.mt

<int32> Maximum training predictors (mandatory, default value is 1000)

-sample.mv

<int32> Maximum validation predictors (mandatory, default value is 1000)

-sample.vtr

<float> Training and validation sample ratio (mandatory, default value is 0.5)

-classifier

<string> Classifier to use for the training [dt/gbt/ann/rf/knn] (mandatory, default value is dt)

-classifier.dt.max

<int32> Maximum depth of the tree (mandatory, default value is 65535)

-classifier.dt.min

<int32> Minimum number of samples in each node (mandatory, default value is 10)

-classifier.dt.ra

<float> Termination criteria for regression tree (mandatory, default value is 0.01)

-classifier.dt.cat

<int32> Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split (mandatory, default value is 10)

-classifier.dt.f

<int32> K-fold cross-validations (mandatory, default value is 10)

-classifier.dt.r

<boolean> Set Use1seRule flag to false (optional, off by default)

-classifier.dt.t

<boolean> Set TruncatePrunedTree flag to false (optional, off by default)

-classifier.gbt.t

<string> Loss Function Type [sqr/abs/hub] (mandatory, default value is sqr)

-classifier.gbt.w

<int32> Number of boosting algorithm iterations (mandatory, default value is 200)

-classifier.gbt.s

<float> Regularization parameter (mandatory, default value is 0.01)

-classifier.gbt.p

<float> Portion of the whole training set used for each algorithm iteration (mandatory, default value is 0.8)

-classifier.gbt.max

<int32> Maximum depth of the tree (mandatory, default value is 3)

-classifier.ann.t

<string> Train Method Type [reg/back] (mandatory, default value is reg)

-classifier.ann.sizes

<string list> Number of neurons in each intermediate layer (mandatory)

-classifier.ann.f

<string> Neuron activation function type [ident/sig/gau] (mandatory, default value is sig)

-classifier.ann.a

<float> Alpha parameter of the activation function (mandatory, default value is 1)

-classifier.ann.b

<float> Beta parameter of the activation function (mandatory, default value is 1)

-classifier.ann.bpdw

<float> Strength of the weight gradient term in the BACKPROP method (mandatory, default value is 0.1)

-classifier.ann.bpms

<float> Strength of the momentum term (the difference between weights on the 2 previous iterations) (mandatory, default value is 0.1)

-classifier.ann.rdw

<float> Initial value Delta_0 of update-values Delta_{ij} in RPROP method (mandatory, default value is 0.1)

-classifier.ann.rdwm

<float> Update-values lower limit Delta_{min} in RPROP method (mandatory, default value is 1e-07)

-classifier.ann.term

<string> Termination criteria [iter/eps/all] (mandatory, default value is all)

-classifier.ann.eps

<float> Epsilon value used in the Termination criteria (mandatory, default value is 0.01)

-classifier.ann.iter

<int32> Maximum number of iterations used in the Termination criteria (mandatory, default value is 1000)

-classifier.rf.max

<int32> Maximum depth of the tree (mandatory, default value is 5)

-classifier.rf.min

<int32> Minimum number of samples in each node (mandatory, default value is 10)

-classifier.rf.ra

<float> Termination Criteria for regression tree (mandatory, default value is 0)

-classifier.rf.cat

<int32> Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split (mandatory, default value is 10)

-classifier.rf.var

<int32> Size of the randomly selected subset of features at each tree node (mandatory, default value is 0)

-classifier.rf.nbtrees <int32>

Maximum number of trees in the forest (mandatory, default value is 100)

-classifier.rf.acc

<float> Sufficient accuracy (OOB error) (mandatory, default value is 0.01)

-classifier.knn.k

<int32> Number of Neighbors (mandatory, default value is 32)

-classifier.knn.rule

<string> Decision rule [mean/median] (mandatory, default value is mean)

-rand

<int32> set user defined seed (optional, off by default)

-inxml

<string> Load otb application from xml file (optional, off by default)

EXAMPLES

otbgui_TrainRegression -io.il training_dataset.tif -io.out regression_model.txt -io.imstat training_statistics.xml -classifier libsvm