In this paper, we present a comprehensive review of recent developments in the application
of machine learning algorithms to Spam filtering, focusing on both textual-and image-based …

Spam is information crafted to be delivered to a large number of recipients, in spite of their
wishes. A spam filter is an automated tool to recognize spam so as to prevent its delivery …

Traditional machine learning makes a basic assumption: the training and test data should be
under the same distribution. However, in many cases, this identical-distribution assumption …

A common assumption in supervised learning is that the input points in the training set follow
the same probability distribution as the input points that will be given in the future test phase …

When training and test samples follow different input distributions (ie, the situation
called\emph {covariate shift}), the maximum likelihood estimator is known to lose its …

Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …

We address the problem of estimating the ratio of two probability density functions, which is
often referred to as the importance. The importance values can be used for various …

We address classification problems for which the training instances are governed by an
input distribution that is allowed to differ arbitrarily from the test distribution—problems also …

A situation where training and test samples follow different input distributions is called
covariate shift. Under covariate shift, standard learning methods such as maximum …

We address classification problems for which the training instances are governed by a
distribution that is allowed to differ arbitrarily from the test distribution---problems also …