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.