Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
Unsupervised clustering is a powerful technique for understanding multispectral and hyperspectral images, being k-means one of the most used iterative approaches. It is a simple th...
Krivine presents the K machine, which produces weak head normal form results. Sestoft introduces several call-by-need variants of the K machine that implement result sharing via pu...
Daniel P. Friedman, Abdulaziz Ghuloum, Jeremy G. S...
This paper presents a model for instruction-level distributed computing that allows the implementation of scalable chip multiprocessors. Based on explicit microthreading it serves ...
Background: Molecular database search tools need statistical models to assess the significance for the resulting hits. In the classical approach one asks the question how probable...
Stefan Wolfsheimer, Inke Herms, Sven Rahmann, Alex...