Question

我正在使用LIBSVM以编程方式进行简单的XOR分类，试图了解这些函数的工作原理。我已按照自述文件中的说明尽可能地设置了问题。使用svm_predict时总是得到错误的输出（总是1或-1）。

在一个相关问题中，有人建议在使用极少数训练样例时可能会出现问题。我尝试将示例数量增加到20但这没有帮助。

我怀疑问题是在prob.x和/或prob.y的定义中的某处，但无法理解在哪里。您能否帮助阐明如何使用svm_node定义prob.x和prob.y？

我彻底搜查但找不到答案......例如Here, here, here, here, and here.

提前致谢！

这是我的代码：

//Parameters
svm_parameter param;
param.svm_type = C_SVC;
param.kernel_type = RBF;
param.degree = 3;
param.gamma = 0;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = 0.4;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;



//Problem definition
svm_problem prob;


//Length
prob.l = 4;                             //number of training examples


//x values

svm_node** x = new svm_node *[prob.l];  //Array of pointers to pointers to arrays

svm_node* x_space1 = new svm_node[3];   //Fist training example
svm_node* x_space2 = new svm_node[3];   //Second training example
svm_node* x_space3 = new svm_node[3];   //Third training example
svm_node* x_space4 = new svm_node[3];   //Fourth training example

x_space1[0].index = 1;                  //Fist training example
x_space1[0].value = 1;
x_space1[1].index = 2;
x_space1[1].value = 1;
x_space1[2].index = -1;

x_space2[0].index = 1;                  //Second training example
x_space2[0].value = 1;
x_space2[1].index = 2;
x_space2[1].value = 0;
x_space2[2].index = -1;

x_space3[0].index = 1;                  //Third training example
x_space3[0].value = 0;
x_space3[1].index = 2;
x_space3[1].value = 1;
x_space3[2].index = -1;

x_space4[0].index = 1;                  //Fourth training example
x_space4[0].value = 0;
x_space4[1].index = 2;
x_space4[1].value = 0;
x_space4[2].index = -1;

x[0] = x_space1;                        //Set each training example to x
x[1] = x_space2;
x[2] = x_space3;
x[3] = x_space4;

prob.x = x;                             //Assign x to the struct field prob.x


//yvalues
prob.y = new double[prob.l];
prob.y[0] = -1;
prob.y[1] = 1;
prob.y[2] = 1;
prob.y[3] = -1;


//Train model
svm_model *model = svm_train(&prob,&param);


//Test model
svm_node* testnode = new svm_node[3];
testnode[0].index = 1;
testnode[0].value = 1;
testnode[1].index = 2;
testnode[1].value = 0;
testnode[2].index = -1;

double retval = svm_predict(model,testnode);
qDebug()<<retval;                               //Should return +1 but returns -1

Answer 1

为什么你的XOR问题是三维的？你不需要每个点的第三个维度（事实上你定义它但不使用它，我不确定libSVM会做什么，但肯定会影响所选的gamma，因为libSVM启发式选择1 / number_of_dimensions）< / LI>
您的C参数看起来很可疑（0.4可能会变低，尝试1000）

Answer 2

您的参数似乎有问题。例如，如果您使用RBF内核，则param.gamma不应为零。

LIBSVM训练数据格式（svm_problem的svm_node中的x值）

2 个答案: