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» Online clustering of parallel data streams
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ESANN
2008
13 years 9 months ago
Parallelizing single patch pass clustering
Clustering algorithms such as k-means, the self-organizing map (SOM), or Neural Gas (NG) constitute popular tools for automated information analysis. Since data sets are becoming l...
Nikolai Alex, Barbara Hammer
HPDC
2008
IEEE
13 years 7 months ago
A two-level scheduler to dynamically schedule a stream of batch jobs in large-scale grids
This paper describes the study conducted to design and evaluate a two-level on-line scheduler to dynamically schedule a stream of sequential and multi-threaded batch jobs on large...
Marco Pasquali, Ranieri Baraglia, Gabriele Capanni...
IPPS
1999
IEEE
13 years 12 months ago
The Characterization of Data-Accumulating Algorithms
A data-accumulating algorithm (d-algorithm for short) works on an input considered as a virtually endless stream. The computation terminates when all the currently arrived data ha...
Stefan D. Bruda, Selim G. Akl
NOSSDAV
2009
Springer
14 years 2 months ago
SLIPstream: scalable low-latency interactive perception on streaming data
A critical problem in implementing interactive perception applications is the considerable computational cost of current computer vision and machine learning algorithms, which typ...
Padmanabhan Pillai, Lily B. Mummert, Steven W. Sch...
ICRA
2008
IEEE
191views Robotics» more  ICRA 2008»
14 years 2 months ago
Combining automated on-line segmentation and incremental clustering for whole body motions
Abstract— This paper describes a novel approach for incremental learning of human motion pattern primitives through on-line observation of human motion. The observed motion time ...
Dana Kulic, Wataru Takano, Yoshihiko Nakamura