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SVM Learning (oSL)
Train SVM and optionally estimate parameters using grid search and cross validation
Inputs
| Name | ID | Description | Type |
|---|---|---|---|
| Input | Input | Training Sample Vectors, as list of list of numbers | Number |
| Output | Output | Training Objective Values | Number |
| C | C | C Parameter, can be estimated by setting PE input | Number |
| G | G | Gamma Parameter, can be estimated by setting PE input | Number |
| Type | Type | Type of SVM: 0=EPSILON-Regression 1=NU-Regression | Integer |
| ParamEstimate | ParEst | Perform Parameter Estimation using Grid Search, Cross Validation and Parallel Computation - ATTENTION CAN TAKE A LOT OF TIME | Boolean |
| TimeLim | TimeLim | Approximate Time Limit for Parameter Estimation in milliseconds, 0 for no limit | Number |
Outputs
| Name | ID | Description | Type |
|---|---|---|---|
| SVM | SVM | Trained Support Vector Machine | SVM |
| C | C | Used C Parameter | Number |
| G | G | Used G Parameter | Number |
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