The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

This tutorial introduces the reader to Gaussian process regression as an expressive tool to
model, actively explore and exploit unknown functions. Gaussian process regression is a …

Gaussian process classification is a popular method with a number of appealing properties.
We show how to scale the model within a variational inducing point framework, out …

This paper is to provide practical options for prognostics so that beginners can select
appropriate methods for their fields of application. To achieve this goal, several popular …

The field of prognostics has attracted significant interest from the research community in
recent times. Prognostics enables the prediction of failures in machines resulting in benefits …

Information theoretic active learning has been widely studied for probabilistic models. For
simple regression an optimal myopic policy is easily tractable. However, for other tasks and …

Kernel methods are among the most popular techniques in machine learning. From a
regularization perspective they play a central role in regularization theory as they provide a …

We investigate the performance of the standard Greedy algorithm for cardinality constrained
maximization of non-submodular nondecreasing set functions. While there are strong …

Sparse Gaussian process methods that use inducing variables require the selection of the
inducing inputs and the kernel hyperparameters. We introduce a variational formulation for …

Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …