Email classification: solution with back propagation technique

To acquire knowledge by learning automatically from the data, through a process of inference, model fitting, or learning from example is one of the rare field of email management. And when an artificial system can perform "intelligent", tasks similar to those performed by the human brain and such is implemented in email classification, such a system will be is extremely intelligent. Using neural network for email content classification with back propagation is where our technique becomes distinct and effective. This paper proposes a new email classification model using a teaching process of multi-layer neural network to implement back propagation algorithm. Our contributions are: the use of empirical analysis to select an optimum, novel collection of features of a user's email message content that enables the rapid detection of most important words, phrases in emails and a demonstration of the effectiveness of two equal sets of emails (training and testing data).