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 (a couple of thousand) 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

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 54 of file KRR.h.

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 CLabels * | apply () |

virtual float64_t | apply (int32_t num) |

virtual bool | load (FILE *srcfile) |

virtual bool | save (FILE *dstfile) |

virtual EClassifierType | get_classifier_type () |

virtual const char * | get_name () const |

## Protected Member Functions | |

virtual bool | train_machine (CFeatures *data=NULL) |

CLabels * apply | ( | ) | ` [virtual]` |

float64_t apply | ( | int32_t | num |
) | ` [virtual]` |

classify one example

**Parameters:**-
*num*which example to classify

**Returns:**- result

Reimplemented from CKernelMachine.

virtual EClassifierType get_classifier_type | ( | ) | ` [virtual]` |

virtual const char* get_name | ( | ) | const` [virtual]` |

bool load | ( | FILE * | srcfile |
) | ` [virtual]` |

bool save | ( | FILE * | dstfile |
) | ` [virtual]` |

void set_tau | ( | float64_t | t |
) |

bool train_machine | ( | CFeatures * | data = `NULL` |
) | ` [protected, virtual]` |

The documentation for this class was generated from the following files:

SHOGUN Machine Learning Toolbox - Documentation