# Given a sample of input/output data, predict new outputs

My problem is the following : I have a number of inputs with the corresponding deterministic outputs. There is no error on either input or output. The link between the two is completely unknown to me. With this information, what kind of mathematical technique should I use to get estimated outputs from new inputs ? Are we talking about machine learning, regression,... ? I am even confused about the mathematical field involved here, as statistics seem to give some tools for this kind of problem, but in my case everything is perfectly deterministic.

• It seems that you need to use neural networks. I'm not versed in these, but there's plenty of info in the internet. – cjferes Sep 4 '14 at 14:55
• Question seems to belong in Cross Validated. By the way, Regression is a part of Machine Learning (supervised learning). – Victor S. Feb 18 at 19:03

Function interpolation is what you are trying to achieve. A standard method is to chose a basis of functions (for example polynomials of your input data, if you do polynomial interpolation) and express your input-output relationship as weighted sums of those functions. If you have $n$ known corresponding points, you can chose a basis of $n$ (independent) functions and find the $n$ weights by solving a linear system of $n$ equations.