  Due to the rapid growth in technology employed by the spammers, there is a
need of classifiers that are more efficient, generic and highly adaptive.
Neural Network based technologies have high ability of adaption as well as
generalization. As per our knowledge, very little work has been done in this
field using neural network. We present this paper to fill this gap. This paper
evaluates performance of three supervised learning algorithms of artificial
neural network by creating classifiers for the complex problem of latest web
spam pattern classification. These algorithms are Conjugate Gradient algorithm,
Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
