|  | ## Logistic Regression

#### Synopsis

MyKLRLearner provides an internal Java implementation of the myKLR by Stefan Rueping.

#### Description

This is the Java implementation of myKLR by Stefan Rüping. myKLR is a tool for large scale kernel logistic regression based on the algorithm of Keerthi/etal/2003 and the code of mySVM.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- weights:
- exampleSet:

#### Parameters

- kernel type: The SVM kernel type
- kernel gamma: The SVM kernel parameter gamma.
- kernel sigma1: The SVM kernel parameter sigma1.
- kernel sigma2: The SVM kernel parameter sigma2.
- kernel sigma3: The SVM kernel parameter sigma3.
- kernel shift: The SVM kernel parameter shift.
- kernel degree: The SVM kernel parameter degree.
- kernel a: The SVM kernel parameter a.
- kernel b: The SVM kernel parameter b.
- kernel cache: Size of the cache for kernel evaluations im MB
- C: The SVM complexity constant. Use -1 for different C values for positive and negative.
- convergence epsilon: Precision on the KKT conditions
- max iterations: Stop after this many iterations
- scale: Scale the example values and store the scaling parameters for test set.
- calculate weights: Indicates if attribute weights should be returned.
- return optimization performance: Indicates if final optimization fitness should be returned as performance.

#### ExampleProcess