支持向量机及解释用法

所属分类:其他
开发工具:matlab
文件大小:982KB
下载次数:3
上传日期:2019-06-23 15:52:19
上 传 者那就好可能忽悠
说明:  支持向量机完整源程序,可MATLAB运行,支持C/C++编写
(Support vector machine complete source, MATLAB can run, support C/C++ preparation)

文件列表:
libsvm-3.23\libsvm-3.23\COPYRIGHT (1497, 2018-07-15)
libsvm-3.23\libsvm-3.23\FAQ.html (83238, 2018-07-15)
libsvm-3.23\libsvm-3.23\heart_scale (27670, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm.java (64084, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm.m4 (63281, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm_model.java (868, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm_node.java (115, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm_parameter.java (1285, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm_print_interface.java (87, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm\svm_problem.java (136, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\libsvm.jar (55181, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\Makefile (659, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\svm_predict.java (4945, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\svm_scale.java (8937, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\svm_toy.java (12262, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\svm_train.java (8354, 2018-07-15)
libsvm-3.23\libsvm-3.23\java\test_applet.html (81, 2018-07-15)
libsvm-3.23\libsvm-3.23\Makefile (732, 2018-07-15)
libsvm-3.23\libsvm-3.23\Makefile.win (1135, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\libsvmread.c (4060, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\libsvmwrite.c (2326, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\make.m (888, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\Makefile (1240, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\svmpredict.c (9818, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\svmtrain.c (11817, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\svm_model_matlab.c (8196, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\svm_model_matlab.h (201, 2018-07-15)
libsvm-3.23\libsvm-3.23\matlab\untitled.slx.autosave (9983, 2019-05-30)
libsvm-3.23\libsvm-3.23\python\commonutil.py (5121, 2018-07-15)
libsvm-3.23\libsvm-3.23\python\Makefile (32, 2018-07-15)
libsvm-3.23\libsvm-3.23\python\svm.py (13631, 2018-07-15)
libsvm-3.23\libsvm-3.23\python\svmutil.py (9395, 2018-07-15)
libsvm-3.23\libsvm-3.23\svm-predict.c (5537, 2018-07-15)
libsvm-3.23\libsvm-3.23\svm-scale.c (8696, 2018-07-15)
libsvm-3.23\libsvm-3.23\svm-toy\qt\Makefile (613, 2018-07-15)
libsvm-3.23\libsvm-3.23\svm-toy\qt\svm-toy.cpp (9722, 2018-07-15)
libsvm-3.23\libsvm-3.23\svm-toy\windows\svm-toy.cpp (11460, 2018-07-15)
... ...

Libsvm is a simple, easy-to-use, and efficient software for SVM classification and regression. It solves C-SVM classification, nu-SVM classification, one-class-SVM, epsilon-SVM regression, and nu-SVM regression. It also provides an automatic model selection tool for C-SVM classification. This document explains the use of libsvm. Libsvm is available at http://www.csie.ntu.edu.tw/~cjlin/libsvm Please read the COPYRIGHT file before using libsvm. Table of Contents ================= - Quick Start - Installation and Data Format - `svm-train' Usage - `svm-predict' Usage - `svm-scale' Usage - Tips on Practical Use - Examples - Precomputed Kernels - Library Usage - Java Version - Building Windows Binaries - Additional Tools: Sub-sampling, Parameter Selection, Format checking, etc. - MATLAB/OCTAVE Interface - Python Interface - Additional Information Quick Start =========== If you are new to SVM and if the data is not large, please go to `tools' directory and use easy.py after installation. It does everything automatic -- from data scaling to parameter selection. Usage: easy.py training_file [testing_file] More information about parameter selection can be found in `tools/README.' Installation and Data Format ============================ On Unix systems, type `make' to build the `svm-train' and `svm-predict' programs. Run them without arguments to show the usages of them. On other systems, consult `Makefile' to build them (e.g., see 'Building Windows binaries' in this file) or use the pre-built binaries (Windows binaries are in the directory `windows'). The format of training and testing data files is:

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