The curse of dimensionality is a significant obstacle to solving dynamic optimization problems by numerical backwards induction when the dimension of the 'state variable' is large.
Curse of dimensionality. Each additional input unit in a network adds another dimension to the space in which the data cases reside. We are attempting to fit a response surface to this data.
The "curse of dimensionality"
Quite generally, larger samples make for smaller variances. Unfortunately, practical considerations prohibit resorting to arbitrarily large samples to bypass the bias-variance problem.
Having discussed the obvious drawback (the curse of dimensionality) for Joint Distributions as a general tool, ...
- A neural network also keeps in check the curse of dimensionality problem that bedevils attempts to model nonlinear functions with large number of variables.
The kernel approach is again employed to address the curse of dimensionality. In the regression method there are considerations based on prior knowledge of the problem and the distribution of the noise.
 See also: Dimensionality, Distribution, Classification, Data mining, Regression
  
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