Perceptron (RapidMiner Studio Core)

Synopsis

This operator learns a linear classifier called Single Perceptron which finds separating hyperplane (if existent). This operator cannot handle polynominal attributes.

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 feed-forward 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. The coming paragraphs explain the basic ideas about neural networks and feed-forward neural networks.

An artificial neural network (ANN), usually called neural network (NN), is a mathematical model or computational model that is inspired by the structure and functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation (the central connectionist principle is that mental phenomena can be described by interconnected networks of simple and often uniform units). In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network during the learning phase. Modern neural networks are usually used to model complex relationships between inputs and outputs or to find patterns in data.

A feed-forward neural network is an artificial neural network where connections between the units do not form a directed cycle. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) to the output nodes. There are no cycles or loops in the network. If you want to use a more sophisticated neural net, please use the Neural Net operator.

Input

training set (Data Table)
The input port expects an ExampleSet. It is the output of the Retrieve operator in the attached Example Process. The output of other operators can also be used as input.

Output

model (Hyperplane Model)
The Hyperplane model is delivered from this output port. This model can now be applied on unseen data sets for the prediction of the label attribute.
example set (Data Table)
The ExampleSet that was given as input is passed without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.

Parameters

roundsThis parameter specifies the number of datascans to use to adapt the hyperplane. Range: integer
learning_rateThis parameter determines how much the weights should be changed at each step. It should not be 0. The hyperplane will adapt to each example with this rate. Range: real

Tutorial Processes

Introduction to Perceptron operator

The 'Ripley' data set is loaded using the Retrieve operator. A breakpoint is inserted here so you can see the data set before the application of the Perceptron operator. You can see that this data set has two regular attributes: att1 and att2. The label attribute has two possible values: 1 and 0. The Perceptron operator is applied on this ExampleSet. All parameters are used with default values. The rounds parameter is set to 3 and the learning rate parameter is set to 0.05. After running the process, you can see the resultant hyperplane model in the Results Workspace.