10 #ifndef _REGRESSIONLIBLINEAR_H___
11 #define _REGRESSIONLIBLINEAR_H___
79 return "LibLinearRegression";
158 void solve_l2r_l1l2_svr(
const liblinear_problem *prob);
161 void init_defaults();
164 void register_parameters();
void set_max_iter(int32_t max_iter)
virtual bool train_machine(CFeatures *data=NULL)
The class Labels models labels, i.e. class assignments of objects.
L2 regularized support vector regression with L1 epsilon tube loss.
void set_liblinear_regression_type(LIBLINEAR_REGRESSION_TYPE st)
Features that support dot products among other operations.
L2 regularized support vector regression with L2 epsilon tube loss.
L2 regularized support vector regression with L2 epsilon tube loss (dual)
LIBLINEAR_REGRESSION_TYPE get_liblinear_regression_type()
#define MACHINE_PROBLEM_TYPE(PT)
int32_t get_max_iter() const
void set_tube_epsilon(float64_t eps)
Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
LIBLINEAR_REGRESSION_TYPE m_liblinear_regression_type
float64_t get_tube_epsilon()
bool get_use_bias() const
all of classes and functions are contained in the shogun namespace
LIBLINEAR_REGRESSION_TYPE
The class Features is the base class of all feature objects.
void set_use_bias(bool use_bias)
virtual ~CLibLinearRegression()
float64_t get_epsilon() const
This class provides an interface to the LibLinear library for large- scale linear learning focusing o...
void set_epsilon(float64_t epsilon)
virtual const char * get_name() const