svm-train - train one or more SVM instance(s) on a given data set to produce a model file

       Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging)

       Please report bugs to the Debian BTS.

svm-train trains a Support Vector Machine to learn the data indicated in the training_set_file
        and produce a model_file
        to save the results of the learning optimization.  This model can be used later with  svm_predict(1)  or
       other LIBSVM enabled software.

       None documented; see Vapnik et al.

       No environment variables.

training_set_file must be prepared in the following simple sparse training vector format:

       <label> <index1>:<value1> <index2>:<value2> . . .

        .

        .

        .

       There is one sample per line.  Each sample consists of a target value (label or regression target)
       followed by a sparse representation of the input vector.  All unmentioned coordinates are assumed to be
       0.  For classification, <label> is an integer indicating the class label (multi-class is supported). For
       regression, <label> is the target value which can be any real number. For one-class SVM, it's not used so
       can be any number.  Except using precomputed kernels (explained in another section), <index>:<value>
       gives a feature (attribute) value.  <index> is an integer starting from 1 and <value> is a real number.
       Indices must be in an ASCENDING order.

       svm-train - train one or more SVM instance(s) on a given data set to produce a model file

       -s svm_type
              svm_type defaults to 0 and can be any value between 0 and 4 as follows:

       0      -- C-SVC1      -- nu-SVC2      -- one-classSVM3      -- epsilon-SVR4      -- nu-SVR

       -t kernel_type
              kernel_type  defaults to 2 (Radial Basis Function (RBF) kernel) and can be any value between 0 and
              4 as follows:

       0      -- linear:u.v1      -- polynomial:(gamma*u.v+coef0)^degree2      -- radialbasisfunction:exp(-gamma*|u-v|^2)3      -- sigmoid:tanh(gamma*u.v+coef0)4      -- precomputedkernel(kernelvaluesintraining_set_file) --

       -d degree
              Sets the degree of the kernel function, defaulting to 3

       -g gamma
              Adjusts the gamma in the kernel function (default 1/k)

       -r coef0
              Sets the coef0 (constant offset) in the kernel function (default 0)

       -c cost
              Sets the parameter C ( cost ) of C-SVC, epsilon-SVR, and nu-SVR (default 1)

       -n nu  Sets the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)

       -p epsilon
              Set the epsilon in the loss function of epsilon-SVR (default 0.1)

       -m cachesize
              Set the cache memory size to cachesize in MB (default 100)

       -e epsilon
              Set the tolerance of termination criterion to epsilon (default 0.001)

       -h shrinking
              Whether to use the shrinking
               heuristics, 0 or 1 (default 1)

       -b probability-estimates
              probability_estimates is a binary value indicating whether to calculate probability estimates when
              training the SVC or SVR model.  Values are 0 or 1 and defaults to 0 for speed.

       -wi weight
              Set the parameter C (cost) of class i to weight*C, for C-SVC (default 1)

       -v n   Set n for n -fold cross validation mode

       -q     quiet mode; suppress messages to stdout.

svm-predict(1), svm-scale(1)

Linux                                               MAY 2006                                        svm-train(1)

svm-train[-ssvm_type][-tkernel_type][-ddegree][-ggamma][-rcoef0][-ccost][-nnu][-pepsilon][-mcachesize][-eepsilon][-hshrinking][-bprobability_estimates]][-wiweight][-vn][-q]training_set_file[model_file]