# 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):****See:**Pseudo-Inverse Algorithm, Numerical Optimization Algorithm, Regression Algorithm, Numerical Analysis.

## References

### 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.