User:Flow~enwiki

Linear Discriminants

What they are: straight line classifiers

Advantages

fastest, simplest possible classifier

Disadvantages

simplistic, and high probability of error, however, this can be overcome/mitigated by

1) transforming the data by some high-dimensional non-linear functions, 
and  creating the straight line in non-linear space (Support Vector Machines)

OR

2) Combining several discriminants when making a classifier by
   i) voting
   ii) aggregation
   iii) Neural Networks (uses some kind of voting?)

Perceptron algorithm

What it does: finds a linear discriminant between two clusters

Advantages

will always find a discriminant, if one exists

Disadvantages

discriminant usually not optimum, or close to optimum
premature halting of the algorithm gives nonsense
may require an arbitrary number of iterations - number of iterations is inversely proportional to distance between clusters

Minimum Square Error (MSE)

What it does: finds a linear discriminant between clusters, by the least squares solution of the distance from (all points in the cluster or only the closest?) to the line

Advantages

will always give a 'reasonable' result, even if the clusters are not separable

Disadvantages

Template Title

What it does:

Advantages

Disadvantages

Some examples of mathematical expressions:

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "http://localhost:6011/en.wikipedia.org/v1/":): {\displaystyle \sin x + \ln y \alpha a^2 }

$\zeta =\cos(\alpha )$

Failed to parse (SVG (MathML can be enabled via browser plugin): Invalid response ("Math extension cannot connect to Restbase.") from server "http://localhost:6011/en.wikipedia.org/v1/":): {\displaystyle T_P = \frac{\pi} {\omega_n \sqrt{1-\zeta^2}} }