Variability of a curve with Vapnik-Chervonenkis dimension 4

Say I have 10,000 data in 2-D and I want to fit a curve to them. There are many functional forms this curve could take -- polynomial, B-spline, trigonometric, and so on. I've decided that I only want to use 4 parameters.

Is there a way to figure out what is the most accurate functional form? That is, considering all possible functions with 4 parameters, which one fits the best in, e.g., an $L_2$ sense?

edit: maybe I should ask about the most accurate function with the same Vapnik-Chervonenkis dimension as a polynomial of degree $4$ rather than "4 parameters"?

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Suppose the function that actually fits your data is $f$ and pick three other random functions $g, h, i$. Then clearly the $4$-parameter family of functions $af + bg + ch + di$ is accurate and you'll want to use $a = 1, b = 0, c = 0, d = 0$. There's no way to reasonably answer this question without explicit constraints on what kind of functions you're willing to use. – Qiaochu Yuan May 20 '11 at 16:03
@Qiaochu Yuan You can't necessarily suppose that $\exists f$ that fits the data. For example $f(3)$ cannot equal both 17 and 177 but (3,17) and (3,177) might both be in the data set. – isomorphismes May 20 '11 at 16:11
@Lao Tzu: the point still stands. There are arbitrarily good answers if you allow yourself arbitrary classes of functions. – Qiaochu Yuan May 20 '11 at 17:40
@Qiaochu Yuan In your example $f(x)$ can change direction 9999 times, which would take a polynomial with much more than 4 parameters. Equally $f$ might expand and shrink its "period" many times more than a sum of 4 trig functions could do. Is there a way to capture this seeming "tradeoff"? I tagged the question with Vapnik-Chervonenkis dimension because I thought maybe there's something deeper in that direction. – isomorphismes May 20 '11 at 20:19
I recommend that you take a look at Support Vector Machines. They have the nice property that they are self-regularizing (i.e. the VC dimension adjusts to the data). – Tpofofn Jun 29 '11 at 3:25