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That's a good point, but after re-reading tel's original comment, I think my statement is still correct. Notice that tel's statement was that "you can over-cons
by rcthompson 13y ago
That's a good point, but after re-reading tel's original comment, I think my statement is still correct. Notice that tel's statement was that "you can over-constrain your model space so that the true model cannot be found". This doesn't necessarily mean constraining your model so that the true model is excluded from your parameter space. If your constraints technically encompass the true solution but only admit an overly complex parametrization of the solution, then it will still reduce (perhaps drastically) your power to find the true model. In this case, "overly complex" means unnecessarily many nonzero (or not almost zero) coefficients in the Fourier series.
- joe_the_user 13y agoMy argument is that there are two kind of situations: * The model could encompass the behavior of the input in a smooth fashion if it's basic parameters are relaxed. * The model would tend to start finding models that are wildly different from the main model at the edges (space and time) if its parameter are relaxed, even if the model would eventually find the real model with enough input and training. one has to handle these two conditions differently, right?