- What is the relationship between the separability of the data and the number of iterations of the PLA?
The number of iterations can grow exponentially as the data groups get close to one another.
- Will the PLA always converge?
Not always, only for linearly separable data.
- Can the PLA converge on non-linearly separable data?
No. However, you can find an acceptable solution by modifying it with the pocket algorithm, for example.
- Why is the perceptron important?
Because it is one of the most fundamental learning strategies that has helped conceive the possibility of learning. Without the perceptron, it could have taken longer for the scientific community to realize the potential of computer-based automatic learning algorithms.
