Network Classifier on Iris data Using MATLAB

Abstract

Classification is the task of assigning object to one of several predefined categories. Classification analysis is the organization of data in given classes. Also known as supervised classification, the classification uses given class labels to order the objects in the data collection. Classification approaches normally use a training set to train the model where all objects are already associated with known class labels. The classification algorithm learns from the training set and builds a model to classify the target,. The classification analysis would generate a model that could be used to find a class (target).
A neural network consists of patterns represented in terms of numerical values attached to the nodes of the graph and transformations between patterns achieved via simple message-passing algorithms. Certain of the nodes in the graph are generally distinguished as being input nodes or output nodes, and the graph as a whole can be viewed as a representation of a multivariate function linking inputs to outputs. Numerical values (weights) are attached to the links of the graph, parameterizing the input/output function and allowing it to be adjusted via a learning algorithm.
Artificial neural networks (ANNs) consider the good tool to learn and classify patterns such as the biological human brain learning process. It consists of simple elements called neurons, which are operating in parallel (included many neuron units that work in parallel). Connections between neurons have weights emerged with the inputs of the neural to give the certain output. The connection's weights have been adjusted during the learning process by iteratively comparing the output of the network and the required target. The ability of the network to show a good performance in the results depends on the training algorithm. There are different types of neural networks, however, most of researches published in the medical studies used one class of neural networks, the back-propagation (BP).