We introduce a method for kinetic characterization of reversible binding of protein onto the inner capillary wall. In essence, a short plug of the protein solution is propagated through the capillary by pressure, and the protein is detected at the distal capillary end. The signal versus time profile is fitted with a numerical model which uses the rate constants of adsorption, kad, and desorptio...

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...

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to a...

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...

In this paper we introduce some tests for the comparison of k samples based on kernel density estimators (KDE), and we develope the Double Minimum method as a new and useful procedure for the crucial problem of bandwidth selection. We study, via Monte Carlo simulations, the statistical power of the proposed tests, as well as the impact of the smoothing degree and the performance of the Double M...

The likelihood for patterns of continuous features needed for probabilistic inference in a Bayesian network classifier (BNC) may be computed by kernel density estimation (KDE), letting every pattern influence the shape of the probability density. Although usually leading to accurate estimation, the KDE suffers from computational cost making it unpractical in many real-world applications. We smo...

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....

FLOSS communities are often described as meritocracies. We consider merit as a social construction that structures the community as a whole by allocating prestige to its participants on the basis of what they do. It implies a hierarchy of the different activities (web maintenance, writing code, bug report...) within the project. We present a study based on the merging of two datasets. We analyz...

The kernel density estimator (KDE) based on a radial positive-semidefinite kernel may be viewed as a sample mean in a reproducing kernel Hilbert space. This mean can be viewed as the solution of a least squares problem in that space. Replacing the squared loss with a robust loss yields a robust kernel density estimator (RKDE). Previous work has shown that RKDEs are weighted kernel density estim...