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 () |
Internally (for linear kernels) it is solved via minimizing the following system
which is boils down to solving a linear system
and in the kernel case
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.
classify regression
output | resulting labels |
Reimplemented from CKernelMachine.
float64_t CKRR::classify_example | ( | int32_t | num | ) | [virtual] |
classify one example
num | which example to classify |
Reimplemented from CClassifier.
virtual EClassifierType CKRR::get_classifier_type | ( | ) | [virtual] |
bool CKRR::load | ( | FILE * | srcfile | ) | [virtual] |
load regression from file
srcfile | file to load from |
Reimplemented from CClassifier.
bool CKRR::save | ( | FILE * | dstfile | ) | [virtual] |
save regression to file
dstfile | file to save to |
Reimplemented from CClassifier.
void CKRR::set_tau | ( | float64_t | t | ) |
bool CKRR::train | ( | ) | [virtual] |