Feature Extraction from web data using Artificial Neural Networks

The main ability of neural network is to learn from its environment and to improve its performance through learning. For this purpose there are two types of learning supervised or active learning – learning with an external ‘teacher’ or a supervisor who present a training set to the network. But another type of learning also exists : unsupervised learning[1] .Unsupervised learning is self organized learning doesn’t require an external teacher. During training session neural network receives a number of input patterns , discovers significant features in these patterns and learns how to classify input data into appropriate categories. It follows the neuro - biological organization of the brain. These algorithms aim to learn rapidly so learn much faster than back-propagation networks and thus can be used in real time. Unsupervised NN are effective in dealing with unexpected and changing conditions[3].