Provided by: otb-bin-qt_6.6.1+dfsg-1build1_amd64 bug

NAME

       otbgui_TrainVectorClassifier - OTB TrainVectorClassifier application

DESCRIPTION

       This  is  the TrainVectorClassifier application, version 5.6.0 Train a classifier based on
       labeled geometries and a list of features to consider.

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

   Parameters:
       -progress <boolean>
              Report progress

        -io.stats               <string>          Input XML image statistics file  (optional, off
       by default)
        -io.confmatout          <string>          Output  confusion  matrix   (optional,  off  by
       default)
        -io.out                <string>         Output model  (mandatory)
        -feat                   <string  list>    Field names for training features.  (mandatory,
       default value is )
        -cfield                <string>         Field containing the  class  id  for  supervision
       (mandatory, default value is class)
        -layer                  <int32>           Layer  Index  (optional, on by default, default
       value is 0)
        -valid.vd               <string>          Validation  Vector  Data   (optional,  off   by
       default)
        -valid.layer            <int32>           Layer  Index  (optional, on by default, default
       value is 0)
        -classifier              <string>           Classifier   to   use   for   the    training
       [boost/dt/gbt/ann/bayes/rf/knn] (mandatory, default value is boost)
        -classifier.boost.t       <string>            Boost   Type   [discrete/real/logit/gentle]
       (mandatory, default value is real)
        -classifier.boost.w    <int32>          Weak count  (mandatory, default value is 100)
        -classifier.boost.r    <float>          Weight Trim Rate  (mandatory,  default  value  is
       0.95)
        -classifier.boost.m     <int32>           Maximum  depth of the tree  (mandatory, default
       value is 1)
        -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.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)
        -rand                  <int32>          set user defined seed  (optional, off by default)
        -inxml                  <string>          Load  otb application from xml file  (optional,
       off by default)

EXAMPLES

       otbgui_TrainVectorClassifier   -io.vd   vectorData.shp   -io.stats   meanVar.xml   -io.out
       svmModel.svm -feat perimeter  area  width -cfield predicted