Neural networks:
- Artificial neural networks.
- They use all the points to make a decision.
Perceptrons:
- 1 → w0, x1 → w1, ..., xn → wn... then go to o = 1, if w0 + sum wi*xi > 0; 0 otherwise.
- Binary classification.
Perceptron training rule:
- Randomly initialize weights.
- Interate through trainint isntances until convergence.
- Update each weight: Delta wi = etha (y-o)xi.
- y is the label, ŷ is the predicted label, o is output, η is the learning rate.
- wi <- wi + Delta wi.
Representional power of perceptrons:
- Perceptrons can represent only linearly separable concepts.
- There is a decision boundary.
- Also as xw > 0.