SHOGUN v0.9.0
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A generic Support Vector Machine Interface.
A support vector machine is defined as
where is the number of training examples
are the weights assigned to each training example
is the kernel and
the bias.
Using an a-priori choosen kernel, the and bias are determined by solving the following quadratic program
here C is a pre-specified regularization parameter.
CSVM | ( | int32_t | num_sv = 0 | ) |
float64_t compute_svm_dual_objective | ( | ) |
float64_t compute_svm_primal_objective | ( | ) |
float64_t * get_linear_term_array | ( | ) | [protected, virtual] |
virtual const char* get_name | ( | void | ) | const [virtual] |
被CMKL、CGMNPSVM、CGNPPSVM、CGPBTSVM、CLaRank、CLibSVM、CLibSVMMultiClass、CLibSVMOneClass、CMPDSVM、CScatterSVM及CLibSVR重载。
bool load | ( | FILE * | svm_file | ) | [virtual] |
bool save | ( | FILE * | svm_file | ) | [virtual] |
void set_defaults | ( | int32_t | num_sv = 0 | ) |
void set_shrinking_enabled | ( | bool | enable | ) |
float64_t* m_linear_term [protected] |
index_t m_linear_term_len [protected] |
bool svm_loaded [protected] |
float64_t tube_epsilon [protected] |
bool use_shrinking [protected] |