文件名称:libsvm-2.89

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是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類,以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEAR include

Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage

Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer

Cross validation for model selection

Probability estimates (logistic regression only)

Weights for unbalanced data

MATLAB/Octave, Java interfaces


(系统自动生成,下载前可以参看下载内容)

下载文件列表

libsvm-2.89

...........\COPYRIGHT

...........\FAQ.html

...........\heart_scale

...........\java

...........\....\libsvm

...........\....\......\svm.java

...........\....\......\svm.m4

...........\....\......\svm_model.java

...........\....\......\svm_node.java

...........\....\......\svm_parameter.java

...........\....\......\svm_print_interface.java

...........\....\......\svm_problem.java

...........\....\libsvm.jar

...........\....\Makefile

...........\....\svm_predict.java

...........\....\svm_scale.java

...........\....\svm_toy.java

...........\....\svm_train.java

...........\....\test_applet.html

...........\Makefile

...........\Makefile.win

...........\python

...........\......\cross_validation.py

...........\......\Makefile

...........\......\README

...........\......\setup.py

...........\......\svm.py

...........\......\svmc.i

...........\......\svmc_wrap.c

...........\......\svm_test.py

...........\......\test_cross_validation.py

...........\README

...........\svm-predict.c

...........\svm-scale.c

...........\svm-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.cpp

...........\svm.h

...........\tools

...........\.....\checkdata.py

...........\.....\easy.py

...........\.....\grid.py

...........\.....\README

...........\.....\subset.py

...........\windows

...........\.......\python

...........\.......\......\svmc.pyd

...........\.......\svm-predict.exe

...........\.......\svm-scale.exe

...........\.......\svm-toy.exe

...........\.......\svm-train.exe

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