SVM_GUI_3.1[mcode]{by-faruto}

代码说明：

  支持向量机SVM(Support Vector Machine）作为一种可训练的机器学习方法，依靠小样本学习后的模型参数进行导航星提取，可以得到分布均匀且恒星数量大为减少的导航星表。 基本情况　Vapnik等人在多年研究统计学习理论基础上对线性分类器提出了另一种设计最佳准则。其原理也从线性可分说起，然后扩展到线性不可分的情况。甚至扩展到使用非线性函数中去，这种分类器被称为支持向量机（Support Vector Machine，简称SVM）。支持向量机的提出有很深的理论背景。 支持向量机方法是在近年来提出的一种新方法。(Support Vector Machine SVM (Support Vector Machine) as a trainable machine learning methods, relying on small samples after learning Navstar extract model parameters can be distributed uniformly and greatly reduced the number of stars the guide star. Basic Vapnik et al in years of study on the basis of statistical learning theory, linear classifier proposed an alternative design best practices. The principle is also linearly separable from talking, and then extended to the case of non-linear. Even extended to use a nonlinear function to go, this classification is known as support vector machines (Support Vector Machine, referred SVM). Support vector machine made a deep theoretical background. Support Vector Machine method is a new method proposed in recent years.)

文件列表：

SVM_GUI_3.1[mcode]{by faruto}
.............................\readme.txt,1142,2011-07-18
.............................\SVC.fig,20001,2011-07-18
.............................\SVC.m,52376,2011-07-18
.............................\SVM_GUI.fig,2753,2011-07-18
.............................\SVM_GUI.m,3675,2010-01-26
.............................\SVR.fig,23716,2011-07-18
.............................\SVR.m,64762,2011-07-18
.............................\testdata
.............................\........\fisheriris_test.mat,911,2010-01-25
.............................\........\regress_test.mat,568,2010-02-02
.............................\........\wine_test.mat,6089,2010-01-26