svm-scale(1) User Manuals svm-scale(1)NAME
svm-scale - scale data to a restricted range as preprocessing for SVM training
SYNOPSIS
svm-scale [ -l lower ] [ -u upper ] [ -y y_upper ] [ -s save_filename ] [ -r restore_filename ] datafilename
DESCRIPTION
svm-scale reads the given datafilename (a training or testing data file as specified in svm_train(1) or svm_predict(1) ) and scales all
dimensions to the given ranges.
OPTIONS -l lower
lower is the lowest (minimal) value allowed in each dimension. It defaults to -1.
-u upper
upper is the highest (maximal) value allowed in each dimension. It defaults to 1.
-y y_lower
y_lower is a boolean value (0 or 1) indicating whether or not y-values (targets) should be scaled. It defaults to 0.
-s save_filename
save_filename indicates the filename to save the scaled data to.
-r restore_filename
restore_filename indicates the filename reserved to hold the original (unscaled) data in case there is a need to restore.
FILES
datafilename must be a training or testing dataset.
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-predict(1)Linux MAY 2006 svm-scale(1)
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SVM-LANDSCAPE(1) User Commands SVM-LANDSCAPE(1)NAME
svm-landscape - Command line interface to svm-landscape in mlpy (version 2.2.0)
SYNOPSIS
svm-landscape [options]
OPTIONS -h, --help
show this help message and exit
-d FILE, --data=FILE
data - required
-s, --standardize
standardize data
-n, --normalize
normalize data
-k K k for k-fold cross validation
-c SETS PAIRS
sets and pairs for monte carlo cross validation
-S, --stratified
for stratified cv
-K KERNEL, --kernel=KERNEL
kernel: 'linear', 'gaussian', 'polynomial', 'tr' [default linear]
-P KPARAMETER, --kparameter=KPARAMETER
kernel parameter (two sigma squared) for gaussian and polynomial kernels [default 0.1]
-o COST, --cost=COST
for cost-sensitive classification [-1.0, 1.0] [default 0.0]
-m MIN, --min=MIN
min value for regularization parameter [default -5]
-M MAX, --max=MAX
max value for regularization parameter [default 5]
-p STEPS, --steps=STEPS
steps for regularization parameter [default 11]
-e SCALE, --scale=SCALE
scale for regularization parameter: 'lin' or 'log' [default log]
-l, --lists
Canberra distance indicator
-a, --auc
Wmw_auc metric computation
mlpy July 2010 SVM-LANDSCAPE(1)
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