CKRR Class Reference
Inheritance diagram for CKRR:
Public Member Functions | |
| CKRR () | |
| CKRR (float64_t tau, CKernel *k, CLabels *lab) | |
| virtual | ~CKRR () |
| void | set_tau (float64_t t) |
| virtual bool | train () |
| virtual CLabels * | classify (CLabels *output=NULL) |
| virtual float64_t | classify_example (int32_t num) |
| virtual bool | load (FILE *srcfile) |
| virtual bool | save (FILE *dstfile) |
| virtual EClassifierType | get_classifier_type () |
Detailed Description
Class KRR implements Kernel Ridge Regression - a regularized least square method for classification and regression. It is similar to support vector machines (cf. CSVM). However in contrast to SVMs a different objective is optimized that leads to a dense solution (thus not only a few support vectors are active in the end but all training examples). This makes it only applicable to rather few training examples. In case a linear kernel is used RR is closely related to Fishers Linear Discriminant (cf. LDA).Internally (for linear kernels) it is solved via minimizing the following system
![\[ \frac{1}{2}\left(\sum_{i=1}^N(y_i-{\bf w}\cdot {\bf x}_i)^2 + \tau||{\bf w}||^2\right) \]](form_88.png)
which is boils down to solving a linear system
![\[ {\bf w} = \left(\tau {\bf I}+ \sum_{i=1}^N{\bf x}_i{\bf x}_i^T\right)^{-1}\left(\sum_{i=1}^N y_i{\bf x}_i\right) \]](form_89.png)
and in the kernel case
![\[ {\bf \alpha}=\left({\bf K}+\tau{\bf I}\right)^{-1}{\bf y} \]](form_90.png)
where K is the kernel matrix and y the vector of labels. The expressed solution can again be written as a linear combination of kernels (cf. CKernelMachine) with bias 
.
Definition at line 50 of file KRR.h.
Constructor & Destructor Documentation
Member Function Documentation
classify regression
- Parameters:
-
output resulting labels
- Returns:
- resulting labels
Reimplemented from CKernelMachine.
| float64_t CKRR::classify_example | ( | int32_t | num | ) | [virtual] |
classify one example
- Parameters:
-
num which example to classify
- Returns:
- result
Reimplemented from CClassifier.
| virtual EClassifierType CKRR::get_classifier_type | ( | ) | [virtual] |
| bool CKRR::load | ( | FILE * | srcfile | ) | [virtual] |
load regression from file
- Parameters:
-
srcfile file to load from
- Returns:
- if loading was successful
Reimplemented from CClassifier.
| bool CKRR::save | ( | FILE * | dstfile | ) | [virtual] |
save regression to file
- Parameters:
-
dstfile file to save to
- Returns:
- if saving was successful
Reimplemented from CClassifier.
| void CKRR::set_tau | ( | float64_t | t | ) |
| bool CKRR::train | ( | ) | [virtual] |
The documentation for this class was generated from the following files: