From the perspective of inductive learning, we are given input samples (x) and output samples (f(x)) and the problem is to estimate the function (f). Specifically, the problem is to generalize from the samples and the mapping to be useful to estimate the output for new samples in the future.
In practice it is almost always too hard to estimate the function, so we are looking for very good approximations of the function.
Some practical examples of induction are :
Credit risk assessment.
* The x is the properties of the customer.
* The f(x) is credit approved or not.
Disease diagnosis.
* The x are the properties of the patient.
* The f(x) is the disease they suffer from.
Face recognition.
* The x are bitmaps of peoples faces.
* The f(x) is to assign a name to the face.
Automatic steering.
* The x are bitmap images from a camera in front of the car.
* The f(x) is the degree the steering wheel should be turned.
