Function Fitting Algorithm

A Function Fitting Algorithm is an algorithm that can be implemented into a function fitting system (to solve a function fitting task).

• AKA: Curve Fitting Algorithm.
• Context:
  • It can range from being a Linear Function Fitting Algorithm to being a Non-Linear Function Fitting Algorithm.
  • It can range from being a Univariate Function Fitting Algorithm to being a Multivariate Function Fitting Algorithm.
  • It can be used as a Model-based Learning Algorithm.
  • …
• Example(s):
  • a Least-Squares Algorithm.
  • Errors-in-Variables Algorithm.
• See: Pseudo-Inverse Algorithm, Numerical Optimization Algorithm, Regression Algorithm, Numerical Analysis.

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References

• http://reference.wolfram.com/mathematica/tutorial/CurveFitting.html

2011

• http://en.wikipedia.org/wiki/Curve_fitting
  • Curve fitting is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, possibly subject to constraints. Curve fitting can involve either interpolation, where an exact fit to the data is required, or smoothing, in which a "smooth" function is constructed that approximately fits the data. A related topic is regression analysis, which focuses more on questions of statistical inference such as how much uncertainty is present in a curve that is fit to data observed with random errors. Fitted curves can be used as an aid for data visualization, to infer values of a function where no data are available, and to summarize the relationships among two or more variables. Extrapolation refers to the use of a fitted curve beyond the range of the observed data, and is subject to a greater degree of uncertainty since it may reflect the method used to construct the curve as much as it reflects the observed data.

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