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IMC
2007
ACM
15 years 5 months ago
Learning network structure from passive measurements
The ability to discover network organization, whether in the form of explicit topology reconstruction or as embeddings that approximate topological distance, is a valuable tool. T...
Brian Eriksson, Paul Barford, Robert Nowak, Mark C...
ICANN
2010
Springer
15 years 4 months ago
Tumble Tree - Reducing Complexity of the Growing Cells Approach
We propose a data structure that decreases complexity of unsupervised competitive learning algorithms which are based on the growing cells structures approach. The idea is based on...
Hendrik Annuth, Christian-A. Bohn
IPSN
2007
Springer
15 years 10 months ago
Dozer: ultra-low power data gathering in sensor networks
Environmental monitoring is one of the driving applications in the domain of sensor networks. The lifetime of such systems is envisioned to exceed several years. To achieve this l...
Nicolas Burri, Pascal von Rickenbach, Roger Watten...
DMIN
2006
125views Data Mining» more  DMIN 2006»
15 years 5 months ago
Privacy-Preserving Bayesian Network Learning From Heterogeneous Distributed Data
In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or ...
Jianjie Ma, Krishnamoorthy Sivakumar
CGF
2000
127views more  CGF 2000»
15 years 3 months ago
Interactive High-Quality Maximum Intensity Projection
Maximum Intensity Projection (MIP) is a volume rendering technique which is used to visualize high-intensity structures within volumetric data. At each pixel the highest data valu...
Lukas Mroz, Helwig Hauser, Eduard Gröller