A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Leaf area index (LAI) is an important indicator of plant growth and yield that can be
monitored by remote sensing. Several models were constructed using datasets derived from …

This is the first comprehensive book on information geometry, written by the founder of the
field. It begins with an elementary introduction to dualistic geometry and proceeds to a wide …

We introduce a new family of positive-definite kernel functions that mimic the computation in
large, multilayer neural nets. These kernel functions can be used in shallow architectures …

Most of the traditional pattern classifiers assume their input data to be well-behaved in terms
of similar underlying class distributions, balanced size of classes, the presence of a full set of …

In this paper, we propose the class-boundaryalignment algorithm to augment SVMs to deal
with imbalanced training-data problems posed by many emerging applications (eg, image …

Due to its occurrence in engineering domains and implications for natural learning, the
problem of utilizing unlabeled data is attracting increasing attention in machine learning. A …

Support vector machines (SVMs) appeared in the early nineties as optimal margin classifiers
in the context of Vapnik's statistical learning theory. Since then SVMs have been …

OBJECTIVE: An important problem that arises in hospitals is the monitoring and detection of
nosocomial or hospital acquired infections (NIs). This paper describes a retrospective …

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes
of disability. Machine learning combined with non-invasive electroencephalography (EEG) …