This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender
systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be
solved using various statistical and machine learning methods, including K nearest neighbours, Bayesian classifiers, the logit
method, discriminant analysis, several kinds of artificial neural networks, support vector machines, classification trees
and other kinds of rule-based methods, as well as random forests and other kinds of classifier ensembles. The book covers
a wide range of available classification methods and their variants, not only those that have already been used in the considered
kinds of applications, but also those that have the potential to be used in them in the future. The book is a valuable resource
for post-graduate students and professionals alike.