msvmocas(1) [debian man page]
MSVMOCAS(1) General Commands Manual MSVMOCAS(1) NAME
msvmocas - train a multi-class linear SVM classifier SYNOPSIS
msvmocas [options] example_file model_file DESCRIPTION
msvmocas is a program that trains a multi-class linear SVM classifier using the Optimized Cutting Plane Algorithm for Support Vector Machines (OCAS) and produces a model file. example_file is a file with training examples in SVM^light format, and model_file is the file in which to store the learned linear rule f(x)=W'*x. model_file contains M columns and D lines, where M is the number of classes and D the number of dimensions, corresponding to the elements of the matrix W [D x M]. OPTIONS
A summary of options is included below. General options: -h Show summary of options. -v (0|1) Set the verbosity level (default: 1) Learning options: -c float Regularization constant C. (default: 1) -n integer Use only the first integer examples for training. By default, integer equals the number of examples in example_file. Optimization options: -m (0|1) Solver to be used: 0 ... standard cutting plane (equivalent to BMRM, SVM^perf) 1 ... OCAS (default) -s integer Cache size for cutting planes. (default: 2000) Stopping conditions: -a float Absolute tolerance TolAbs: halt if QP-QD <= TolAbs. (default: 0) -r float Relative tolerance TolAbs: halt if QP-QD <= abs(QP)*TolRel. (default: 0.01) -q float Desired objective value QPValue: halt is QP <= QPValue. (default: 0) -t float Halts if the solver time (loading time is not counted) exceeds the time given in seconds. (default: infinity) EXAMPLES
Train the multi-class SVM classifier from example file example4_train.light, with the regularization constant C=10, verbosity switched off, and save model to msvmocas.model: msvmocas -c 10 -v 0 example4_train.light msvmocas.model Compute the testing error of the classifier stored in msvmocas.model with linclass(1) using testing examples from example4_test.light and save the predicted labels to example4_test.pred: linclass -e -o example4_test.pred example4_test.light msvmocas.model SEE ALSO
svmocas(1), linclass(1). AUTHORS
msvmocas was written by Vojtech Franc <xfrancv@cmp.felk.cvut.cz> and Soeren Sonnenburg <Soeren.Sonnenburg@tu-berlin.de>. This manual page was written by Christian Kastner <debian@kvr.at>, for the Debian project (and may be used by others). June 16, 2010 MSVMOCAS(1)
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OPENCV_HAARTRAINING(1) User Commands OPENCV_HAARTRAINING(1) NAME
opencv_haartraining - train classifier SYNOPSIS
opencv_haartraining [options] DESCRIPTION
opencv_haartraining is training the classifier. While it is running, you can already get an impression, whether the classifier will be suitable or if you need to improve the training set and/or parameters. In the output: 'POS:' shows the hitrate in the set of training samples (should be equal or near to 1.0 as in stage 0) 'NEG:' indicates the false alarm rate (should reach at least 5*10-6 to be a usable classifier for real world applications) If one of the above values gets 0 (zero) there is an overflow. In this case the false alarm rate is so low, that further training doesn't make sense anymore, so it can be stopped. OPTIONS
opencv_haartraining supports the following options: -data dir_name The directory in which the trained classifier is stored. -vec vec_file_name The file name of the positive samples file (e.g. created by the opencv_createsamples(1) utility). -bg background_file_name The background description file (the negative sample set). It contains a list of images into which randomly distorted versions of the object are pasted for positive sample generation. -bg-vecfile This option is that bgfilename represents a vec file with discrete negatives. The default is not set. -npos number_of_positive_samples The number of positive samples used in training of each classifier stage. The default is 2000. -nneg number_of_negative_samples The number of negative samples used in training of each classifier stage. The default is 2000. Reasonable values are -npos 7000 -nneg 3000. -nstages number_of_stage The number of stages to be trained. The default is 14. -nsplits number_of_splits Determine the weak classifier used in stage classifiers. If the value is 1, then a simple stump classifier is used >=2, then CART classifier with number_of_splits internal (split) nodes is used The default is 1. -mem memory_in_MB Available memory in MB for precalculation. The more memory you have the faster the training process is. The default is 200. -sym, -nonsym Specify whether the object class under training has vertical symmetry or not. Vertical symmetry speeds up training process and reduces memory usage. For instance, frontal faces show off vertical symmetry. The default is -sym. -minhitrate min_hit_rate The minimal desired hit rate for each stage classifier. Overall hit rate may be estimated as min_hit_rate^number_of_stages. The default is 0.950000. -maxfalsealarm max_false_alarm_rate The maximal desired false alarm rate for each stage classifier. Overall false alarm rate may be estimated as max_false_alarm_rate^number_of_stages. The default is 0.500000. -weighttrimming weight_trimming Specifies whether and how much weight trimming should be used. The default is 0.950000. A decent choice is 0.900000. -eqw Specify if initial weights of all samples will be equal. -mode {BASIC|CORE|ALL} Select the type of haar features set used in training. BASIC uses only upright features, while CORE uses the full upright feature set and ALL uses the full set of upright and 45 degree rotated feature set. The default is BASIC. For more information on this see http://www.lienhart.de/ICIP2002.pdf. -h sample_height The sample height (must have the same value as used during creation). The default is 24. -w sample_width The sample width (must have the same value as used during creation). The default is 24. -bt {DAB|RAB|LB|GAB} The type of the applied boosting algorithm. You can choose between Discrete AdaBoost (DAB), Real AdaBoost (RAB), LogitBoost (LB) and Gentle AdaBoost (GAB). The default is GAB. -err {misclass|gini|entropy} The type of used error if Discrete AdaBoost (-bt DAB) algorithm is applied. The default is misclass. -maxtreesplits max_number_of_splits_in_tree_cascade The maximal number of splits in a tree cascade. The default is 0. -minpos min_number_of_positive_samples_per_cluster The minimal number of positive samples per cluster. The default is 500. The same information is shown, if opencv_haartraining is called without any arguments/options. EXAMPLES
TODO SEE ALSO
opencv_createsamples(1), opencv_performance(1) More information and examples can be found in the OpenCV documentation. AUTHORS
This manual page was written by Daniel Leidert <daniel.leidert@wgdd.de> and Nobuhiro Iwamatsu <iwamatsu@debian.org> for the Debian project (but may be used by others). OpenCV May 2010 OPENCV_HAARTRAINING(1)