Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …

The majority of patients in the hospital are ambulatory and would benefit significantly from
predictive and personalized monitoring systems. Such patients are well suited to having …

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been
successfully used to classify groups of individuals based on their patterns of brain activity or …

Abstract ASHRAE Global Thermal Comfort Database has been extensively used for
analyzing specific thermal comfort parameters or models, evaluating subjective metrics, and …

Adverse hospital patient outcomes due to deterioration are often preceded by periods of
physiological deterioration that is evident in the vital signs, such as heart rate, respiratory …

Novelty detection, or one-class classification, is of particular use in the analysis of high-
integrity systems, in which examples of failure are rare in comparison with the number of …

It is generally assumed that one-class machine learning techniques can not reach the
performance level of two-class techniques. The importance of this work is that while one …

In this work we present a new support vector machine (SVM)-based method for fMRI data
analysis. SVM has been shown to be a powerful, efficient data-driven tool in pattern …

Background: Neuroimaging techniques combined with computational neuroanatomy have
been playing a role in the investigation of healthy aging and Alzheimer's disease (AD). The …