|  | ## K-NN

#### Synopsis

Classification with k-NN based on an explicit similarity measure.

#### Description

A k nearest neighbor implementation.

#### Input

- training set: expects: ExampleSet, expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- k: The used number of nearest neighbors.
- weighted vote: Indicates if the votes should be weighted by similarity.
- measure types: The measure type
- mixed measure: Select measure
- nominal measure: Select measure
- numerical measure: Select measure
- divergence: Select divergence
- kernel type: The kernel type
- kernel gamma: The kernel parameter gamma.
- kernel sigma1: The kernel parameter sigma1.
- kernel sigma2: The kernel parameter sigma2.
- kernel sigma3: The kernel parameter sigma3.
- kernel degree: The kernel parameter degree.
- kernel shift: The kernel parameter shift.
- kernel a: The kernel parameter a.
- kernel b: The kernel parameter b.

#### ExampleProcess