Description: 支持向量机的理论和应用，不过是英文的，不知道可不可以啊！不过很全面的，要看看啊！-SVM theory and application, but English is not possible to know ah! But it is comprehensive, to see ah! Platform: |
Size: 256000 |
Author:韩乐 |
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Description: SVM支持向量机 里面包含一个教程，包括支持向量机的原理，也可以直接看接口函数直接调用-SVM support vector machine which contains a tutorial, including Support Vector Machine principle, can also look directly interface function directly ca Platform: |
Size: 1024000 |
Author:miaowei |
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Description: 关于matlab主元分析的一个简单的实例，可供初学者参考学习。-Matlab PCA on a simple example of reference for beginners to learn. Platform: |
Size: 3072 |
Author:刘明 |
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Description: MATLAB的SVM算法实现，Matlab Support Vector Machine Toolbox，This toolbox was designed as a teaching aid, which matlab is
particularly good for since source code is relatively legible and
simple to modify. However, it is still reasonably fast if used
with the supplied optimiser. However, if you really want to speed
things up you should consider compiling the matrix composition
routine for H into a mex function. Then again if you really want
to speed things up you probably shouldn t be using matlab
anyway... Get hold of a dedicated C program once you understand
the algorithm.-MATLAB-SVM algorithm, Matlab Support Vector Machine Toolbox, This toolbox was designed as a teaching aid, which matlab isparticularly good for since source code is relatively legible andsimple to modify. However, it is still reasonably fast if usedwith the supplied optimiser. However , if you really want to speedthings up you should consider compiling the matrix compositionroutine for H into a mex function. Then again if you really wantto speed things up you probably shouldn t be using matlabanyway ... Get hold of a dedicated C program once you understandthe algorithm. Platform: |
Size: 128000 |
Author:chenbin |
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Description: MATLAB函数参考手册，查看matlab函数作用以及功能。- SVMLSPex02.m
Two Dimension SVM Problem, Two Class and Separable Situation
Difference with SVMLSPex01.m:
Take the Largrange Function (16)as object function insteads ||W||,
so it need more time than SVMLSex01.m
Method from Christopher J. C. Burges:
"A Tutorial on Support Vector Machines for Pattern Recognition", page 9
Objective: min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16)
Subject to: sum{ai*yi}=0 ,function (15)
and ai>=0 for any i, the particular set of constraints C2 (page 9, line14).
The optimizing variables is "Lagrange Multipliers": A=[a1,a2,...,am],m is the number of total samples. Platform: |
Size: 561152 |
Author:王东东 |
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Search - svm tutorial matlab