There is a whole lot of difference between curve fitting and curve fitting with performance guarantees on future data under a distribution free (limited depende

There is a whole lot of difference between curve fitting and curve fitting with performance guarantees on future data under a distribution free (limited dependence model). BTW the 'machine learning' term is Russian coinage and its genesis lies in non-paramteric statistics, the key result that sparked it all off was Vapnik and Chervonenkis's result that is essentially a much generalized and non-asymptotic version of Glivenko Cantelli. The other result was that of Stone that showed universal algorithms that can achieve the Bayes error in the limit not only exist but also constructed such an algorithm. This was the first time it was established that 'learning' is possible.