perceptron
Sign in to saveIn machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that 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.
Article
22 sectionsContents
- History
- Mark I Perceptron machine
- ''Principles of Neurodynamics'' (1962)
- ''Perceptrons'' (1969)
- Subsequent work
- Definition
- Power of representation
- Information theory
- Boolean function
- Universal approximation theorem
- Conjunctively local perceptron
- Learning algorithm for a single-layer perceptron
- Definitions
- Steps
- Convergence of one perceptron on a linearly separable dataset
- Perceptron cycling theorem
- Learning a Boolean function
- Variants
- Multiclass perceptron
- References
- Further reading
- External links
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that 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.
==History==