|  | ## Relevance Vector Machine

#### Synopsis

An implementation of a relevance vector machine.

#### Description

Relevance Vector Machine (RVM) Learner. The RVM is a probabilistic method both for classification and regression. The implementation of the relevance vector machine is based on the original algorithm described by Tipping/2001. The fast version of the marginal likelihood maximization (Tipping/Faul/2003) is also available if the parameter "rvm_type" is set to "Constructive-Regression-RVM".

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- rvm type: Regression RVM
- kernel type: The type of the kernel functions.
- 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 iteration: The maximum number of iterations used.
- min delta log alpha: Abort iteration if largest log alpha change is smaller than this
- alpha max: Prune basis function if its alpha is bigger than this
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess