**2.2 Neural network classification**

The methods implemented for neural network classification are
explained below.

**Multilayer Perceptrons:**

A Back propagation network or Multilayer perceptron consists of at
least three layers of units: an input layer, at least one intermediate hidden
layer, and an output layer. The units are connected in a feed-forward fashion.
With Back propagation networks, learning occurs during a training phase. After
a Back propagation network has learned the correct classification for a set of
inputs, it can be tested on a second set of inputs to see how well it
classifies untrained patterns.

**Radial-Basis Function Networks:**

Radial basis function networks are also feed forward, but have
only one hidden layer. RBF hidden layer units have a receptive field, which has
a center that is, a particular input value at which they have a maximal output.
Their output tails off as the input moves away from this point. Generally, the
hidden unit function is a Gaussian.

**Support Vector Machines:**

Support vector machine (SVM) is a popular technique for
classification. Given a training set of instance-label pairs (xi, yi), i = 1, .
. . , 1 where xi e Rn and ye{l, -1}', the support vector machines Here training
vectors » are mapped into a higher (maybe infinite) dimensional space by the
function (j). Then SVM finds a linear separating hyper plane with the maximal
margin in this higher dimensional space. C > 0 is the penalty parameter of
the error term. Furthermore, K(xi, Xj) = <KX')'<I> (Xj) is called the
kernel function.K- Nearest Neighbor Classifier:Among statistical approaches, a
k-nearest neighbor classifier was selected because it does not assume any
underlying distribution of data. In the k-nearest neighbor rule, a test sample
is assigned the class most frequently represented among the k nearest training
samples. If two or more such classes exist, then the test sample is assigned
the class with minimum average distance to it.