|  | ## Perceptron

#### Synopsis

Single Perceptron finding seperating hyperplane if one exists

#### Description

The perceptron is a type of artificial neural network invented in 1957 by Frank Rosenblatt. It can be seen as the simplest kind of feedforward neural network: a linear classifier. Beside all biological analogies, the single layer perceptron is simply a linear classifier which is efficiently trained by a simple update rule: for all wrongly classified data points, the weight vector is either increased or decreased by the corresponding example values.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- rounds: The number of datascans used to adapt the hyperplane.
- learning rate: The hyperplane will adapt with this rate to each example.

#### ExampleProcess