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» Evaluating algorithms that learn from data streams
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EVOW
2007
Springer
15 years 10 months ago
Evaluation of Different Metaheuristics Solving the RND Problem
RND (Radio Network Design) is a Telecommunication problem consisting in covering a certain geographical area by using the smallest number of radio antennas achieving the biggest co...
Miguel A. Vega-Rodríguez, Juan Antonio G&oa...
NPL
2000
95views more  NPL 2000»
15 years 6 months ago
Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping
Generative topographic mapping (GTM) is a statistical model to extract a hidden smooth manifold from data, like the self-organizing map (SOM). Although a deterministic search algo...
Akio Utsugi
ICML
2005
IEEE
16 years 7 months ago
Supervised versus multiple instance learning: an empirical comparison
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
Soumya Ray, Mark Craven
DPD
2002
168views more  DPD 2002»
15 years 6 months ago
Evolutionary Algorithms for Allocating Data in Distributed Database Systems
A major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring relations (fragments) accessed by a query from different sites...
Ishfaq Ahmad, Kamalakar Karlapalem, Yu-Kwong Kwok,...
KDD
2004
ACM
113views Data Mining» more  KDD 2004»
16 years 7 months ago
Learning spatially variant dissimilarity (SVaD) measures
Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure betwee...
Krishna Kummamuru, Raghu Krishnapuram, Rakesh Agra...