svmbr_20090226
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开发工具:C++
文件大小:870KB
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上传日期:2013-02-27 00:00:37
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说明:  SVn training program

文件列表:
svmbr (0, 2009-02-27)
svmbr\uml (0, 2009-02-27)
svmbr\test (0, 2009-02-27)
svmbr\src (0, 2009-02-27)
svmbr\Release (0, 2009-02-27)
svmbr\docs (0, 2009-02-27)
svmbr\.svn (0, 2009-02-27)
svmbr\uml\.svn (0, 2009-02-27)
svmbr\test\.svn (0, 2009-02-27)
svmbr\src\.svn (0, 2009-02-27)
svmbr\Release\.svn (0, 2009-02-27)
svmbr\docs\.svn (0, 2009-02-27)
svmbr\.svn\tmp (0, 2009-02-27)
svmbr\.svn\props (0, 2009-02-27)
svmbr\.svn\prop-base (0, 2009-02-27)
svmbr\.svn\text-base (0, 2009-02-27)
svmbr\uml\.svn\tmp (0, 2009-02-27)
svmbr\uml\.svn\props (0, 2009-02-27)
svmbr\uml\.svn\prop-base (0, 2009-02-27)
svmbr\uml\.svn\text-base (0, 2009-02-27)
svmbr\test\.svn\tmp (0, 2009-02-27)
svmbr\test\.svn\props (0, 2009-02-27)
svmbr\test\.svn\prop-base (0, 2009-02-27)
svmbr\test\.svn\text-base (0, 2009-02-27)
svmbr\src\.svn\tmp (0, 2009-02-27)
svmbr\src\.svn\props (0, 2009-02-27)
svmbr\src\.svn\prop-base (0, 2009-02-27)
svmbr\src\.svn\text-base (0, 2009-02-27)
svmbr\Release\.svn\tmp (0, 2009-02-27)
svmbr\Release\.svn\props (0, 2009-02-27)
svmbr\Release\.svn\prop-base (0, 2009-02-27)
svmbr\Release\.svn\text-base (0, 2009-02-27)
svmbr\docs\.svn\tmp (0, 2009-02-27)
svmbr\docs\.svn\props (0, 2009-02-27)
svmbr\docs\.svn\prop-base (0, 2009-02-27)
svmbr\docs\.svn\text-base (0, 2009-02-27)
svmbr\.svn\tmp\props (0, 2009-02-27)
svmbr\.svn\tmp\prop-base (0, 2009-02-27)
svmbr\.svn\tmp\text-base (0, 2009-02-27)
svmbr\uml\.svn\tmp\props (0, 2009-02-27)
... ...

+----------------------------------------------+ | SVMBR - A program for training SVMs | +----------------------------------------------+ | * Creator, modeling, coding: | | Marcelo Barros de Almeida | | barros@smar.com.br | | http://litc.cpdee.ufmg.br/~barros/ | | * Coding, improvements, bug fixes: | | Bernardo Penna | | bprc@brfree.com.br | +----------------------------------------------+ Copyright(c) 2002 by Marcelo Barros de Almeida All rights reserved ------------------------------------------------ Install notes ------------------------------------------------ Download the SMOBR and unpack it in a new directory using gunzip/tar or winzip. Windows users SMOBR can be compiled using DJGPP (free GCC based compiler) or Visual C++. For DJGPP there exist an interface called RHIDE (like the old Borland C for DOS). I renamed libstdcxx to libstdc++ to compile in DJGPP. Use Makefile provided with sources. For Visual C++ there is a project ready to use. Checks if there is a definition for _MSC_VER and WIN32 (or define it yourself with #define _MSC_VER). Unix/Linux users make all ;-) ------------------------------------------------ Using SVMBR ------------------------------------------------ Usage:(1 of 2) svmbr -option1 value1 -option2 value2 ... Options (type in lower case !): KERNELS: ======= -k [poly|rbf|perceptron|linear] | Chooses the SVM kernel. | Kernel expressions: | rbf : exp(-|x1-x2|^2/(2*p3*p1^2)) | poly : ((x1*x2+p2)/p3)^p1 | perceptron: tanh(p1*x1*x2/p3+p2) | linear : (x1*x2+p2)/p3 | -p1 value | Kernel parameters. -p2 value | For RBF, p1 is the variance and p2 -p3 value | is not used. For polynomial p1 is the | degree, p2 is bias. Perceptron has p1 as | slope and p2 as offset. Linear uses p2 as | bias. Scale parameter is provided for | all kernels by p3 parameter. | Default: p1=1, p2=0, p3=1. | FILES: ===== -tp filename | Training patterns. | First two numbers specifies the amount of | training vectors and the input vector, | dimension, respectively. After these two | values, other points are considered as | data. Put one data vector per row, | followed by its target. Numbers must be | separated by spaces. For instance, dataset | for XOR problem is stated as (please, | remove comments): | ---------------------------------------- | 4 // number of training vector | 2 // input dimension | 0 0 -1 // vector 1 + target | 0 1 1 // vector 2 + target | 1 0 1 // vector 3 + target | 1 1 -1 // vector 4 + target | ---------------------------------------- | -gp filename | Testing patterns (generalization). | First two numbers specifies the amount of | testing vectors and the input vector, | dimension, respectively. After these two | values, other points are considered as | data. Put one data vector per row, | followed by its target. Numbers must be | separated by spaces. For instance, dataset | for XOR problem is stated as (please, | remove comments): | ---------------------------------------- | 4 // number of training vector | 2 // input dimension | 0 0 -1 // vector 1 + target | 0 1 1 // vector 2 + target | 1 0 1 // vector 3 + target | 1 1 -1 // vector 4 + target | ---------------------------------------- | -go filename | Testing output file. | Generalization results are saved on this | file, like SVM's output and MSE. | -sv filename | SVM configuration. | This file describes the SVM structure. It | is the output file from SVMBR. | -d [normal|sparse|binary] | Data representation. | This parameter affects how the vectors are | treated by the program. Set sparse when | using training vector built mainly by | zeros and binary when input vectors have | only 0 and 1 values. | Default: d=normal | OPTIMIZATION: ============ -solver [edr|smo|boosting] | Method used to find the solution. | Default: solver = smo | -c value | Upper limit for Lagrange multipliers. | Default: c=1 | -t value | Tolerance for KKT condition. | Default: t=0.001 | -e value | Epsilon for bound precision. | Default: e=0.001 | -edr value | Error dependent repetition. | Number of scans performed on the training | set. | Default: edr = -1 (off) | -power value | Power for comparison function in EDR. | (err_i)^power > j*err_max/edr. | Default: power = 1 | -maxiter value | Maximum number of iterations of SMO when | using together with EDR. | Default: maxiter = -1 | -chunk value | Chunking size. | Number of vectors in chunk. | Default: chunk = -1 (not using chunk). | OTHERS: ====== -l [0|1] | Hard (1) or soft limit (0) when | evaluating output. | Default: l=0 | -version | Print the current version. | -h | Print this help message. | 3) Usage:(2 of 2) svmbr file_with_all_parameters Options (type in upper case !): [GLOBAL] NUMCLASSES=value | Number of classes. | Default: 2 | DATAREPRESENTATION= | Data representation. [normal|sparse|binary] | This parameter affects how the vectors are | treated by the program. Set sparse when | using training vector built mainly by | zeros and binary when input vectors have | only 0 and 1 values. | Default: d=normal | SOLUTIONMETHOD=[edr|smo|boosting]| Method used to find the solution. | Default: smo | CHUNKSIZE=value | Chunking size. | Number of vectors in chunk. | Default: chunk = -1 (not using chunk). | TRAININGPATTERNFILE=filename | Training patterns. | First two numbers specifies the amount of | training vectors and the input vector, | dimension, respectively. After these two | values, other points are considered as | data. Put one data vector per row, | followed by its target. Numbers must be | separated by spaces. For instance, dataset | for XOR problem is stated as (please, | remove comments): | ---------------------------------------- | 4 // number of training vector | 2 // input dimension | 0 0 -1 // vector 1 + target | 0 1 1 // vector 2 + target | 1 0 1 // vector 3 + target | 1 1 -1 // vector 4 + target | ---------------------------------------- | For multi class problems, the targets are | represented by 0,1,2,3... For instance: | 6 // number of training vector | 2 // input dimension | 0 7 0 // vector 1 + target | 2 1 0 // vector 2 + target | 4 4 1 // vector 3 + target | 3 1 2 // vector 4 + target | 2 4 0 // vector 5 + target | 7 6 1 // vector 6 + target | TESTINGPATTERNFILE=filename | Testing patterns (generalization). | First two numbers specifies the amount of | testing vectors and the input vector, | dimension, respectively. After these two | values, other points are considered as | data. Put one data vector per row, | followed by its target. Numbers must be | separated by spaces. For instance, dataset | for XOR problem is stated as (please, | remove comments): | ---------------------------------------- | 4 // number of training vector | 2 // input dimension | 0 0 -1 // vector 1 + target | 0 1 1 // vector 2 + target | 1 0 1 // vector 3 + target | 1 1 -1 // vector 4 + target | ---------------------------------------- | For multi class problems, the targets are | represented by 0,1,2,3... For instance: | 6 // number of training vector | 2 // input dimension | 0 7 0 // vector 1 + target | 2 1 0 // vector 2 + target | 4 4 1 // vector 3 + target | 3 1 2 // vector 4 + target | 2 4 0 // vector 5 + target | 7 6 1 // vector 6 + target | TESTINGOUTFILE=filename | Testing output file. | Generalization results are saved on this | file, like SVM's output and MSE. | [GLOBAL] or [CLASS:x] (x is the number of the class: 0,1,2,3...) SVMFILE=filename | SVM configuration. | This file describes the SVM structure. It | is the output file from SVMBR | Only for 2 classes it is global, if not | it is not global, it has to exist in each | class parameters. [CLASS:x] (x is the number of the class: 0,1,2,3...) KERNELTYPE= | Chooses the SVM kernel. [poly|rbf|perceptron|linear] | Kernel expressions: | rbf : exp(-|x1-x2|^2/(2*p3*p1^2)) | poly : ((x1*x2+p2)/p3)^p1 | perceptron: tanh(p1*x1*x2/p3+p2) | linear : (x1*x2+p2)/p3 | P1=value | Kernel parameters. P2=value | For RBF, p1 is the variance and p2 P3=value | is not used. For polynomial p1 is the | degree, p2 is bias. Perceptron has p1 as | slope and p2 as offset. Linear uses p2 as | bias. Scale parameter is provided for | all kernels by p3 parameter. | Default: P1=1, P2=0, P3=1. | | C=value | Upper limit for Lagrange multipliers. | Default: 1 ... ...

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