Transfer Learning From Multiple Source Domains via Consensus Regularization


 
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Old 09-22-2008
Transfer Learning From Multiple Source Domains via Consensus Regularization

HPL-2008-115 Transfer Learning From Multiple Source Domains via Consensus Regularization - Luo, Ping; Zhuang, Fuzhen; Xiong, Hui; Xiong, Yuhong; He, Qing
Keyword(s): Classification, Transfer Learning, Consensus Regularization
Abstract: Recent years have witnessed an increased interest in transfer learning. Despite the vast amount of research performed in this field, there are remaining challenges in applying the knowledge learnt from multiple source domains to a target domain. First, data from multiple source domains can be semant ...
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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)