Moving object detection based on monitoring video system is often a challenging problem. Specially to monitor traffic at both day and night, in different weather and illumination conditions and with changeable background. Kernel Density Estimation (KDE) model is an effective approach to judge background and foreground, however, typical KDE uses fixed parameters, such as bandwidths, threshold, e...

We evaluate the risks of cardiovascular disease to the Chinese population by i) detecting ”abnormality” using 3 one-class classification methods (a discriminative one-class support vector machine (SVM), a generative kernel density estimate (KDE), and a discriminative KDE), and ii) predicting probabilities of ”normality”, arrhythmia, and ischemia using 3class classification method (a discriminat...

Exploratory data analysis (EDA) can reveal important features of underlying distributions, and these features often have an impact on inferences and conclusions drawn from data. Graphical analysis is central to EDA, and graphical representations of distributions often benefit from smoothing. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (K...

When highly-accurate and/or assumption-free density estimation is needed, nonpara-metric methods are often called upon-most notably the popular kernel density estimation (KDE) method. However, the practitioner is instantly faced with the formidable computational cost of KDE for appreciable dataset sizes, which becomes even more prohibitive when many models with diierent kernel scales (bandwidth...

Imbalanced class distribution affects many applications in machine learning, including medical diagnostics, text classification, intrusion detection and others. In this paper, we propose a novel ensemble classification method designed to deal with imbalanced data. The proposed trains each tree the using uniquely generated synthetically balanced data balancing is carried out via kernel density e...

Kernel density estimation (KDE) is a widely used method in geography to study concentration of point pattern data. Geographical networks are 1.5 dimensional spaces with specific characteristics, analyzing events occurring on (accidents roads, leakages pipes, species along rivers, etc.). In the last decade, they required extension spatial KDE. Several versions Network KDE (NKDE) have been propos...

People armed with more information have a huge advantage over people who less information. While some believe that learning needed to become successful would take years, even regular reading for an hour day and self-education can be easy way expand knowledge appreciably increase success rates. American entrepreneur, motivational speaker visionary Jack Canfield's book "The Success Principles:&nb...

The Semantic Desktop brings the desktop data to a standardized form, enabling it to be better interlinked and as a result easier to browse and search. It is lifted from application specific formats and locations and made available to all the desktop applications. However, some applications might be better suited to display some resource types than others, or might provide specialized functions....