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KDD
2009
ACM
611views Data Mining» more  KDD 2009»
14 years 9 months ago
Fast approximate spectral clustering
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...
Donghui Yan, Ling Huang, Michael I. Jordan
SBCCI
2003
ACM
129views VLSI» more  SBCCI 2003»
14 years 2 months ago
Hyperspectral Images Clustering on Reconfigurable Hardware Using the K-Means Algorithm
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...
Abel Guilhermino S. Filho, Alejandro César ...
LISP
2007
74views more  LISP 2007»
13 years 9 months ago
Improving the lazy Krivine machine
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...
SAMOS
2004
Springer
14 years 2 months ago
Scalable Instruction-Level Parallelism.
This paper presents a model for instruction-level distributed computing that allows the implementation of scalable chip multiprocessors. Based on explicit microthreading it serves ...
Chris R. Jesshope
BMCBI
2011
13 years 4 months ago
Accurate statistics for local sequence alignment with position-dependent scoring by rare-event sampling
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...