文件名称:bsvm-2.08
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [C/C++] [源码]
- 上传时间：
- 2017-04-18
- 文件大小：
- 364kb
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- 程
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BSVM解决了支持向量机（SVM），用于解决大型分类和回归问题。 它包括以下方法
一个对一个使用约束约束公式的多类分类
通过解决单一优化问题（再次，有界公式）进行多类分类。 参见我们比较文件的第3节。
使用Crammer和Singer的配方进行多级分类。 参见我们的比较文章第4节。
使用约束约束公式的回归-BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes the following methods
One vs. One multi-class classification using a bound-constrained formulation
Multi-class classification by solving a single optimization problem (again, a bounded formulation). See Section 3 of our comparison paper.
Multi-class classification using Crammer and Singer s formulation. See Section 4 of our comparison paper.
Regression using a bound-constrained formulation
Multi-class classification using Crammer and Singer s formulation with squared hinge (L2) loss
一个对一个使用约束约束公式的多类分类
通过解决单一优化问题（再次，有界公式）进行多类分类。 参见我们比较文件的第3节。
使用Crammer和Singer的配方进行多级分类。 参见我们的比较文章第4节。
使用约束约束公式的回归-BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes the following methods
One vs. One multi-class classification using a bound-constrained formulation
Multi-class classification by solving a single optimization problem (again, a bounded formulation). See Section 3 of our comparison paper.
Multi-class classification using Crammer and Singer s formulation. See Section 4 of our comparison paper.
Regression using a bound-constrained formulation
Multi-class classification using Crammer and Singer s formulation with squared hinge (L2) loss
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下载文件列表
bsvm-2.08\bsvm.cpp
.........\dtron\dbreakpt.c
.........\.....\dcauchy.c
.........\.....\dgpnrm.c
.........\.....\dgpstep.c
.........\.....\dprecond.c
.........\.....\dprsrch.c
.........\.....\dspcg.c
.........\.....\dtron.c
.........\.....\dtrpcg.c
.........\.....\dtrqsol.c
.........\.....\Makefile
.........\.....\misc.c
.........\f2c\blas.h
.........\...\blasp.h
.........\...\dasum.c
.........\...\daxpy.c
.........\...\dcopy.c
.........\...\ddot.c
.........\...\dgemv.c
.........\...\dnrm2.c
.........\...\dpotf2.c
.........\...\dscal.c
.........\...\dsymv.c
.........\...\dtrsv.c
.........\...\f2c.h
.........\...\lsame.c
.........\...\Makefile
.........\...\xerbla.c
.........\Makefile
.........\Makefile.win
.........\README
.........\solvebqp.c
.........\svm-predict.c
.........\svm-scale.c
.........\....toy\gtk\callbacks.cpp
.........\.......\...\callbacks.h
.........\.......\...\interface.c
.........\.......\...\interface.h
.........\.......\...\main.c
.........\.......\...\Makefile
.........\.......\...\svm-toy.glade
.........\.......\qt\Makefile
.........\.......\..\svm-toy.cpp
.........\.......\windows\svm-toy.cpp
.........\svm-train.c
.........\svm.h
.........\tools\checkdata.py
.........\.....\easy.py
.........\.....\grid.py
.........\.....\README
.........\.....\subset.py
.........\vehicle.scale
.........\windows\bsvm-predict.exe
.........\.......\bsvm-train.exe
.........\.......\svm-scale.exe
.........\.......\svm-toy.exe
.........\svm-toy\gtk
.........\.......\qt
.........\.......\windows
.........\dtron
.........\f2c
.........\svm-toy
.........\tools
.........\windows
bsvm-2.08