|  | ## Support Vector Machine (PSO)

#### Synopsis

PsoSVM uses a Particle Swarm Optimization for optimization.

#### Description

This is a SVM implementation using a particle swarm optimization (PSO) approach to solve the dual optimization problem of a SVM. It turns out that on many datasets this simple implementation is as fast and accurate as the usual SVM implementations.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- show convergence plot: Indicates if a dialog with a convergence plot should be drawn.
- 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.
- C: The SVM complexity constant (0: calculates probably good value).
- max evaluations: Stop after this many evaluations
- generations without improval: Stop after this number of generations without improvement (-1: optimize until max_iterations).
- population size: The population size (-1: number of examples)
- inertia weight: The (initial) weight for the old weighting.
- local best weight: The weight for the individual's best position during run.
- global best weight: The weight for the population's best position during run.
- dynamic inertia weight: If set to true the inertia weight is improved during run.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess