|  | ## Gaussian Process

#### Synopsis

An implementation of Gaussian Processes.

#### Description

Gaussian Process (GP) Learner. The GP is a probabilistic method both for classification and regression.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- kernel type: The kind of kernel.
- kernel lengthscale: The lengthscale used in all kernels.
- kernel degree: The degree used in the poly kernel.
- kernel bias: The bias used in the poly kernel.
- kernel sigma1: The SVM kernel parameter sigma1 (Epanechnikov, Gaussian Combination, Multiquadric).
- kernel sigma2: The SVM kernel parameter sigma2 (Gaussian Combination).
- kernel sigma3: The SVM kernel parameter sigma3 (Gaussian Combination).
- kernel shift: The SVM kernel parameter shift (Multiquadric).
- kernel a: The SVM kernel parameter a (neural).
- kernel b: The SVM kernel parameter b (neural).
- max basis vectors: Maximum number of basis vectors to be used.
- epsilon tol: Tolerance for gamma induced projections
- geometrical tol: Tolerance for geometry induced projections

#### ExampleProcess