Perceptron learning rule
Definition taught in Neural units, part of Statistical learning
Perceptron learning rule
| Type | Definition |
| Taught in | Neural units |
| Part | Statistical learning |
| Questions using it | 1 |
Let the training pairs be (x_i,y_i) with y_i\in\{0,1\} and with each x_i augmented by x_{i0}=1. Start from any weights w, including the bias. Repeat until no pattern is misclassified. For each pattern i compute o_i=f(w^\top x_i) and set
w\leftarrow w+(y_i-o_i)\,x_i.
Used in
- Taught in Neural units, where the derivation and examples are.
- Questions that use it (1) — Perceptron by hand
- All 7 questions on Neural units
See also
Artificial neuron · A single threshold unit cannot compute XOR · One hidden layer approximates continuous functions · History of the neural unit · Neural unit · Linear separability · Threshold unit without a bias · Threshold unit with a bias