svm-predict(1) [debian man page]
svm-predict(1) User Manuals svm-predict(1) NAME
svm-predict - make predictions based on a trained SVM model file and test data SYNOPSIS
svm-predict [ -b probability_estimates ] [ -q ] test_data model_file [ output_file ] DESCRIPTION
svm-predict uses a Support Vector Machine specified by a given input model_file to make predictions for each of the samples in test_data The format of this file is identical to the training_data file used in svm_train(1) and is just a sparse vector as follows: <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. If you have label data avail- able for testing then you can enter these values in the test_data file. If they are not available you can just enter 0 and will not know real accuracy for the SVM directly, however you can still get the results of its prediction for the data point. If output_file is given, it will be used to specify the filename to store the predicted results, one per line, in the same order as the test_data file. OPTIONS
-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. -q quiet mode; suppress messages to stdout. FILES
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 consist 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. ENVIRONMENT
No environment variables. DIAGNOSTICS
None documented; see Vapnik et al. BUGS
Please report bugs to the Debian BTS. AUTHOR
Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging) SEE ALSO
svm-train(1), svm-scale(1) Linux MAY 2006 svm-predict(1)
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svm-train(1) User Manuals svm-train(1) NAME
svm-train - train one or more SVM instance(s) on a given data set to produce a model file SYNOPSIS
svm-train [-s svm_type ] [ -t kernel_type ] [ -d degree ] [ -g gamma ] [ -r coef0 ] [ -c cost ] [ -n nu ] [ -p epsilon ] [ -m cachesize ] [ -e epsilon ] [ -h shrinking ] [ -b probability_estimates ] ] [ -wi weight ] [ -v n ] [ -q ] training_set_file [ model_file ] DESCRIPTION
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. OPTIONS
-s svm_type svm_type defaults to 0 and can be any value between 0 and 4 as follows: 0 -- C-SVC 1 -- nu-SVC 2 -- one-class SVM 3 -- epsilon-SVR 4 -- 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.v 1 -- polynomial: (gamma*u.v + coef0)^degree 2 -- radial basis function: exp(-gamma*|u-v|^2) 3 -- sigmoid: tanh(gamma*u.v + coef0) 4 -- precomputed kernel (kernel values in training_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. FILES
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. ENVIRONMENT
No environment variables. DIAGNOSTICS
None documented; see Vapnik et al. BUGS
Please report bugs to the Debian BTS. AUTHOR
Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging) SEE ALSO
svm-predict(1), svm-scale(1) Linux MAY 2006 svm-train(1)