公有成员 | 保护属性

CLibSVR类参考


详细描述

Class LibSVR, performs support vector regression using LibSVM.

The SVR solution can be expressed as

\[ f({\bf x})=\sum_{i=1}^{N} \alpha_i k({\bf x}, {\bf x_i})+b \]

where $\alpha$ and $b$ are determined in training, i.e. using a pre-specified kernel, a given tube-epsilon for the epsilon insensitive loss, the follwoing quadratic problem is minimized (using sequential minimal decomposition (SMO))

\begin{eqnarray*} \max_{{\bf \alpha},{\bf \alpha}^*} &-\frac{1}{2}\sum_{i,j=1}^N(\alpha_i-\alpha_i^*)(\alpha_j-\alpha_j^*){\bf x}_i^T {\bf x}_j -\sum_{i=1}^N(\alpha_i+\alpha_i^*)\epsilon - \sum_{i=1}^N(\alpha_i-\alpha_i^*)y_i\\ \mbox{wrt}:& {\bf \alpha},{\bf \alpha}^*\in{\bf R}^N\\ \mbox{s.t.}:& 0\leq \alpha_i,\alpha_i^*\leq C,\, \forall i=1\dots N\\ &\sum_{i=1}^N(\alpha_i-\alpha_i^*)y_i=0 \end{eqnarray*}

Note that the SV regression problem is reduced to the standard SV classification problem by introducing artificial labels $-y_i$ which leads to the epsilon insensitive loss constraints *

\begin{eqnarray*} {\bf w}^T{\bf x}_i+b-c_i-\xi_i\leq 0,&\, \forall i=1\dots N\\ -{\bf w}^T{\bf x}_i-b-c_i^*-\xi_i^*\leq 0,&\, \forall i=1\dots N \end{eqnarray*}

with $c_i=y_i+ \epsilon$ and $c_i^*=-y_i+ \epsilon$

在文件LibSVR.h51行定义。

继承图,类CLibSVR
Inheritance graph
[图例]

所有成员的列表。

公有成员

 CLibSVR ()
 CLibSVR (float64_t C, float64_t epsilon, CKernel *k, CLabels *lab)
virtual ~CLibSVR ()
virtual bool train (CFeatures *data=NULL)
virtual EClassifierType get_classifier_type ()
virtual const char * get_name () const

保护属性

svm_problem problem
svm_parameter param
struct svm_model * model

构造及析构函数文档

CLibSVR (  )

default constructor

在文件LibSVR.cpp16行定义。

CLibSVR ( float64_t  C,
float64_t  epsilon,
CKernel k,
CLabels lab 
)

constructor

参数:
Cconstant C
epsilonepsilon
kkernel
lablabels

在文件LibSVR.cpp22行定义。

~CLibSVR (  ) [virtual]

在文件LibSVR.cpp33行定义。


成员函数文档

virtual EClassifierType get_classifier_type (  ) [virtual]

get classifier type

返回:
classifie type LIBSVR

重载CClassifier

在文件LibSVR.h81行定义。

virtual const char* get_name (  ) const [virtual]
返回:
object name

重载CSVM

在文件LibSVR.h84行定义。

bool train ( CFeatures data = NULL ) [virtual]

train regression

参数:
datatraining data (parameter can be avoided if distance or kernel-based regressor are used and distance/kernels are initialized with train data)
返回:
whether training was successful

重载CClassifier

在文件LibSVR.cpp38行定义。


成员数据文档

struct svm_model* model [protected]

SVM model

在文件LibSVR.h92行定义。

svm_parameter param [protected]

SVM parameter

在文件LibSVR.h89行定义。

svm_problem problem [protected]

SVM problem

在文件LibSVR.h87行定义。


该类的文档由以下文件生成:

SHOGUN Machine Learning Toolbox - Documentation