In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.
Mark I Perceptron machine, the first implementation of the perceptron algorithm. It was connected to a camera with 20×20 cadmium sulfide photocells to make a 400-pixel image. The main visible feature is the sensory-to-association plugboard, which sets different combinations of input features. To the right are arrays of potentiometers that implemented the adaptive weights.
The Mark 1 Perceptron, being adjusted by Charles Wightman (Mark I Perceptron project engineer). Sensory units at left, association units in center, and control panel and response units at far right. The sensory-to-association plugboard is behind the closed panel to the right of the operator. The letter "C" on the front panel is a display of the current state of the sensory input.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.
Clustering via Large Indel Permuted Slopes, CLIPS, turns the alignment image into a learning regression problem. The varied slope (b) estimates between each pair of DNA segments enables to identify segments sharing the same set of indels.
A decision tree showing survival probability of passengers on the Titanic
