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于 2020-07-08 发布 文件大小:600KB
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  图像处理模式识别一种分类算法：svm，对于所提取的图像的特征进行训练学习然后分类。(Image processing, pattern recognition of a classification algorithm: SVM trained to learn the characteristics of the extracted image and then classified.)

文件列表：

svm程序
.......\libsvm-3.11
.......\...........\libsvm-3.11
.......\...........\...........\A.mat,8925,2012-04-08
.......\...........\...........\B.mat,10063,2012-04-08
.......\...........\...........\htm" target=_blank>COPYRIGHT,1497,2012-04-06
.......\...........\...........\FAQ.html,71213,2012-04-06
.......\...........\...........\htm" target=_blank>heart_scale,27670,2012-04-06
.......\...........\...........\java
.......\...........\...........\....\libsvm
.......\...........\...........\....\......\svm.java,62406,2012-04-06
.......\...........\...........\....\......\svm.m4,61755,2012-04-06
.......\...........\...........\....\......\svm_model.java,734,2012-04-06
.......\...........\...........\....\......\svm_node.java,115,2012-04-06
.......\...........\...........\....\......\svm_parameter.java,1288,2012-04-06
.......\...........\...........\....\......\svm_print_interface.java,87,2012-04-06
.......\...........\...........\....\......\svm_problem.java,136,2012-04-06
.......\...........\...........\....\libsvm.jar,50037,2012-04-06
.......\...........\...........\....\Makefile,624,2012-04-06
.......\...........\...........\....\svm_predict.java,4267,2012-04-06
.......\...........\...........\....\svm_scale.java,8944,2012-04-06
.......\...........\...........\....\svm_toy.java,11483,2012-04-06
.......\...........\...........\....\svm_train.java,8268,2012-04-06
.......\...........\...........\....\test_applet.html,81,2012-04-06
.......\...........\...........\Makefile,559,2012-04-06
.......\...........\...........\Makefile.win,1087,2012-04-06
.......\...........\...........\matlab
.......\...........\...........\......\libsvmread.c,4014,2012-04-06
.......\...........\...........\......\libsvmwrite.c,2148,2012-04-06
.......\...........\...........\......\make.m,799,2012-04-06
.......\...........\...........\......\Makefile,1499,2012-04-06
.......\...........\...........\......\htm" target=_blank>README,9618,2012-04-06
.......\...........\...........\......\svmpredict.c,9263,2012-04-06
.......\...........\...........\......\svmtrain.c,11371,2012-04-06
.......\...........\...........\......\svm_model_matlab.c,7722,2012-04-06
.......\...........\...........\......\svm_model_matlab.h,201,2012-04-06
.......\...........\...........\python
.......\...........\...........\......\Makefile,32,2012-04-06
.......\...........\...........\......\htm" target=_blank>README,11051,2012-04-06
.......\...........\...........\......\svm.py,8602,2012-04-06
.......\...........\...........\......\svmutil.py,8119,2012-04-06
.......\...........\...........\htm" target=_blank>README,27193,2012-04-06
.......\...........\...........\svm-predict.c,5381,2012-04-06
.......\...........\...........\svm-scale.c,7042,2012-04-06
.......\...........\...........\svm-toy
.......\...........\...........\.......\gtk
.......\...........\...........\.......\...\callbacks.cpp,9742,2012-04-06
.......\...........\...........\.......\...\callbacks.h,1765,2012-04-06
.......\...........\...........\.......\...\interface.c,6457,2012-04-06
.......\...........\...........\.......\...\interface.h,203,2012-04-06
.......\...........\...........\.......\...\main.c,398,2012-04-06
.......\...........\...........\.......\...\Makefile,573,2012-04-06
.......\...........\...........\.......\...\svm-toy.glade,6402,2012-04-06
.......\...........\...........\.......\qt
.......\...........\...........\.......\..\Makefile,392,2012-04-06
.......\...........\...........\.......\..\svm-toy.cpp,9153,2012-04-06
.......\...........\...........\.......\windows
.......\...........\...........\.......\.......\svm-toy.cpp,10807,2012-04-06
.......\...........\...........\svm-train.c,8891,2012-04-06
.......\...........\...........\svm.cpp,62947,2012-04-06
.......\...........\...........\svm.def,434,2012-04-06
.......\...........\...........\svm.h,3129,2012-04-06
.......\...........\...........\tools
.......\...........\...........\.....\checkdata.py,2478,2012-04-06
.......\...........\...........\.....\easy.py,2699,2012-04-06
.......\...........\...........\.....\grid.py,11850,2012-04-06
.......\...........\...........\.....\htm" target=_blank>README,4728,2012-04-06
.......\...........\...........\.....\subset.py,3004,2012-04-06
.......\...........\...........\windows
.......\...........\...........\.......\libsvm.dll,140800,2012-04-06
.......\...........\...........\.......\libsvmread.mexw32,8192,2012-04-06
.......\...........\...........\.......\libsvmread.mexw64,10752,2012-04-06
.......\...........\...........\.......\libsvmwrite.mexw32,7680,2012-04-06
.......\...........\...........\.......\libsvmwrite.mexw64,9216,2012-04-06
.......\...........\...........\.......\svm-predict.exe,107008,2012-04-06
.......\...........\...........\.......\svm-scale.exe,80384,2012-04-06
.......\...........\...........\.......\svm-toy.exe,140800,2012-04-06
.......\...........\...........\.......\svm-train.exe,135680,2012-04-06
.......\...........\...........\.......\svmpredict.mexw32,20480,2012-04-06
.......\...........\...........\.......\svmpredict.mexw64,24064,2012-04-06
.......\...........\...........\.......\svmtrain.mexw32,49152,2012-04-06
.......\...........\...........\.......\svmtrain.mexw64,62976,2012-04-06
.......\thrshowsvm.m,504,2012-04-06

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0 个回复

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    加权核范数最小化应用于图像处理的代码，很实用，且是图像处理领域很新的方法源码，所以该方法效果很好，希望对大家有用。(Nuclear weighted norm minimization codes used in image processing, it is useful, and it is a new image processing method for the source, so that this method works well, want to be useful.)
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    积分：1
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    2012-12-06 21:00:50下载
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    2014-05-05 11:02:44下载
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    实现了基于关键像素点的FLICM图像分割算法，关键像素由局部最大值方法提取。(A FLICM image segmentation algorithm based on key pixels is implemented. Key pixels are extracted by local maximum method.)
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    2012-09-12 22:31:14下载
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