支持向量機(jī)及其編程實(shí)現(xiàn)

1、 3. 利用SVM的C語言開發(fā)包編寫自己的SVM分類器： svm_model *svm_train(const svm_problem *prob, const svm_parameter *param double svm_predict(const svm_model *model, const svm_node *x struct svm_problem int l; double *y; struct svm_node *x; struct svm_node int index; double value; 1 1:-0.844335 2:-0.184362 3:0.046215 4:

2、0.593243 5:0.140576 6:0.330716 7:0.172408 8:-0.105515 struct svm_parameter int svm_type; int kernel_type; double degree; / for poly double gamma; / for poly/rbf/sigmoid double coef0; / for poly/sigmoid/ these are for training only double cache_size; / in MB double eps; / stopping criteria double C;

3、int nr_weight; int *weight_label; double* weight; / for C_SVC double nu; / for NU_SVC, ONE_CLASS, and NU_SVR double p; / for EPSILON_SVR int shrinking; / use the shrinking heuristics ; struct svm_model svm_parameter param; / parameter int nr_class; / number of classes int l; / total #SV svm_node *SV

4、; / SVs (SVl double *sv_coef; / coefficients for SVs in decision functions double *rho; / constants in decision int *label; / label of each class (labeln int *nSV; / number of SVs for each class (nSVn int free_sv; / 1 if svm_model is created by svm_load_model / 0 if svm_model is created by svm_train

5、 ; svm_type c_svc kernel_type linear nr_class 2 total_sv 239 rho 0.672633 label 1 -1 nr_sv 119 120 SV 1 1:-0.844335 2:-0.184362 3:0.046215 4:0.593243 5:0.140576 6:0.330716 7:0.172408 8:-0.105515 1 1:2.123396 2:-0.309469 3:0.769512 4:1.220115 5:-0.692439 6:1.878123 7:1.367596 8:0.999905 -0.185 1:-1.141108 2:0.409897 3:-0.573754 4:-0.033629 5:1.832638 6:-0.709344 7:-0.361805 8:-1.04087 0.399 1:1.52