The Perceptron learning law
{"Image DocumentName": "The Perceptron learning law", "Image ImageDescription": "Learning is a recursive procedure of modifying weights from a given set of input-output patterns. For a single perceptron, the objective of the learning (encoding) is to find the decision plane, (that is, the related weight vector), which separates two classes of given input-output training vectors. The perceptron learning procedure is an example of a supervised error-correcting learning law.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}