Question

I need a machine learning algorithm which takes some training samples of form (x,y),
and compute approximate function f:X->Y such that the error is minimum. error is defined as the difference b/n y and f(x).

But this learning algorithm must be a iterative one,and As the no.of iterations increases, the error must be decreased.

Any example would be helpful.

Answer 1

Neural network is one algorithm that have two features:
1. It can train iterativly on new data
2. It can train on same data iterativly, so error is decreased with each iteration. (back propagation learning)