Support Vector Machine (Linear)
(AI Studio Core)
Synopsis
This operator provides a linear Support Vector Machine based upon the JMySVM.Description
This learner uses the Java implementation of the support vector machine mySVM by Stefan Rüping. It is restricted to the dot (linear) kernel, but outputs a high performance model that only contains the linear coefficient for faster model application.
Input
training set (IOObject)
Output
model (Kernel Model)- example set (Data Table)
This is an example set output port
Parameters
- kernel cacheSize of the cache for kernel evaluations im MB
- CThe SVM complexity constant. Use -1 for different C values for positive and negative.
- convergence epsilonPrecision on the KKT conditions
- max iterationsStop after this many iterations
- scaleScale the example values and store the scaling parameters for test set.
- L posA factor for the SVM complexity constant for positive examples
- L negA factor for the SVM complexity constant for negative examples
- epsilonInsensitivity constant. No loss if prediction lies this close to true value
- epsilon plusEpsilon for positive deviation only
- epsilon minusEpsilon for negative deviation only
- balance costAdapts Cpos and Cneg to the relative size of the classes
- quadratic loss posUse quadratic loss for positive deviation
- quadratic loss negUse quadratic loss for negative deviation