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文件名称:libsvm-3.1-[FarutoUltimate3.1Mcode]

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  • 其他小程序
  • 资源属性：
  • [MacOS] [Matlab] [源码]
  • 上传时间：
  • 2017-10-19
  • 文件大小：
  • 1.16mb
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修正介绍说明

介绍说明－－下载内容均来自于网络，请自行研究使用

态势要素获取作为整个网络安全态势感知的基础，其质量的好坏将直接影响态势感知系统的性能。针对态势要素不易获取问题，提出了一种基于增强型概率神经网络的层次化框架态势要素获取方法。在该层次化获取框架中，利用主成分分析(PCA)对训练样本属性进行约简并对特殊属性编码融合处理，将其结果用于优化概率神经网络(PNN)结构，降低系统复杂度。以PNN作为基分类器，基分类器通过反复迭代、权重更替，然后加权融合处理形成最终的强多分类器。实验结果表明，该方案是有效的态势要素获取方法并且精确度达到95.53%，明显优于文中其他算法，有较好的泛化能力。(As the basis of the whole network security situation awareness, the quality of situation elements extraction will directly affect the performance of the situation awareness system. To solve the problem that the situation element is difficult to extract, we propose a method to extract the hierarchical fr a me situation elements based on the enhanced probabilistic neural network. In the hierarchical access fr a me, we use the principal component analysis (PCA) to reduct the training sample attribute and to process the special attribute encoding fusion. The result can be used to optimize the structure of the probabilistic neural network (PNN) and reduce the system complexity. Take PNN as the base classifier to form the final strong classifier by repeated iteration, weight replacement and weighted fusion. The experimental results show that the scheme is an effective method to obtain the situation factors and its accuracy is 95.53%,which is significantly better than other algorithms.)
相关搜索： 网络安全；态势要素

(系统自动生成,下载前可以参看下载内容)

下载文件列表

tools\checkdata.py

tools\easy.py

tools\grid.py

tools\README

tools\subset.py

windows\libsvm.dll

windows\libsvmread.mexw32

windows\libsvmread.mexw64

windows\libsvmwrite.mexw32

windows\libsvmwrite.mexw64

windows\svm-predict.exe

windows\svm-scale.exe

windows\svm-toy.exe

windows\svm-train.exe

windows\svmpredict.mexw32

windows\svmpredict.mexw64

windows\svmtrain.mexw32

windows\svmtrain.mexw64

svm.cpp.bak

svm-predict.c

svm-scale.c

svm-train.c

svm.h

svm.cpp

svm.def

FAQ.html

Makefile.win

COPYRIGHT

heart_scale

Makefile

README

java\libsvm\svm.java

java\libsvm\svm.m4

java\libsvm\svm_model.java

java\libsvm\svm_node.java

java\libsvm\svm_parameter.java

java\libsvm\svm_print_interface.java

java\libsvm\svm_problem.java

java\libsvm.jar

java\Makefile

java\svm_predict.java

java\svm_scale.java

java\svm_toy.java

java\svm_train.java

java\test_applet.html

matlab\heart_scale.mat

matlab\libsvmread.c

matlab\libsvmread.mexw32

matlab\libsvmwrite.c

matlab\libsvmwrite.mexw32

matlab\make.m

matlab\Makefile

matlab\README

matlab\svm.obj

matlab\svmpredict.c

matlab\svmpredict.mexw32

matlab\svmtrain.c

matlab\svmtrain.c.bak

matlab\svmtrain.mexw32

matlab\svm_model_matlab.c

matlab\svm_model_matlab.h

matlab\svm_model_matlab.obj

matlab-implement[by faruto]\a_template_flow_usingSVM_class.m

matlab-implement[by faruto]\a_template_flow_usingSVM_regress.m

matlab-implement[by faruto]\ClassResult.m

matlab-implement[by faruto]\ClassResult_test.m

matlab-implement[by faruto]\gaSVMcgForClass.m

matlab-implement[by faruto]\gaSVMcgForRegress.m

matlab-implement[by faruto]\gaSVMcgpForRegress.m

matlab-implement[by faruto]\libsvm参数说明.txt

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\bs2rv.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\contents.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\crtbase.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\crtbp.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\crtrp.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\migrate.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\mpga.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\mut.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\mutate.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\mutbga.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\mytest\gaSVM.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\ranking.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\recdis.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\recint.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\reclin.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\recmut.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\recombin.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\reins.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\rep.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\resplot.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\rws.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\scaling.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\select.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\sus.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\xovdp.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\xovdprs.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\xovmp.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\xovsh.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\xovshrs.m

matlab-implement[by faruto]\myprivate\gatbx[Sheffield]\xovsp.m

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