發信人: jeff814 (mimi), 信區: DataMining
標  題: osu svm中的Parameters說明
發信站: 南京大學小百合站 (Fri Jun 13 16:00:02 2003)

整理了一下，供使用osu svm的朋友參考


參數Parameters：


|Kernel Type| Degree | Gamma | Coefficient | C |Cache size|epsilon| SVM Type |
	nu (nu-svm) |loss toleration | shrinking |		


Kernel Type:

 0 --- Linear   1 --- Polynomial   2 --- RBF    3 --- Sigmoid


degree: parameter needed for kernel of type polynomial (default:3)


Gamma: parameter needed for all kernels except linear. If the input value is z
ero, Gamma will be set defautly as 1/(max_pattern_dimension) in the function. 
If the input value is non-zero, Gamma will remain unchanged in the function.(d
efault: 1)


Coefficient: parameter needed for kernels of type polynomial and sigmoid (defa
ult:0)


C（線性不可分情況下對錯分樣本的懲罰系數）: Cost of the constrain violation (fo
r C-SVC & C-SVR) (default 1), or the nu for the nu-svm and 1-svm in the second
 stage. This nu should smaller than the nu defined in Parameter(9)


Cache Size: as the buffer to hold the <X(:,i),X(:,j)> (in MB)


epsilon（松弛項）: tolerance of termination criterion


SVM Type: (Recommend only using 0, which is c-SVM classifier)(default: 0)

                   0 --- c-SVM classifier

                   1 --- nu-SVM classifier

                   2 --- one-class SVM  （分布估計distribution estimation）

                   3 --- epsilon-SVR 

                   4 --- nu-SVR


nu: the nu of nu-SVM used in the boundary finding process。nu of nu-SVC, one-c
lass SVM, and nu-SVR (default: 0.5)


loss toleration: epsilon in loss function of epsilon-SVR (default: 0.1) 


shrinking: whether to use the shrinking heuristics, 0 or 1 (default: 1)


Percentage（SVMPlot2.m）: the percentage of input train samples used for bound
ary finding.	





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