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» Using Classifiers to Solve Warehouse Location Problems
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TC
2002
14 years 9 months ago
Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks
We present novel grid coverage strategies for effective surveillance and target location in distributed sensor networks. We represent the sensor field as a grid (two or three-dimen...
Krishnendu Chakrabarty, S. Sitharama Iyengar, Hair...
FLAIRS
2001
14 years 11 months ago
Improvement of Nearest-Neighbor Classifiers via Support Vector Machines
Theoretically well-founded, Support Vector Machines (SVM)are well-knownto be suited for efficiently solving classification problems. Althoughimprovedgeneralization is the maingoal...
Marc Sebban, Richard Nock
ICMLA
2010
14 years 7 months ago
Using Randomised Vectors in Transcription Factor Binding Site Predictions
Finding the location of binding sites in DNA is a difficult problem. Although the location of some binding sites have been experimentally identified, other parts of the genome may ...
Faisal Rezwan, Yi Sun, Neil Davey, Rod Adams, Alis...
ICANN
2007
Springer
15 years 1 months ago
Resilient Approximation of Kernel Classifiers
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
Thorsten Suttorp, Christian Igel
EOR
2007
88views more  EOR 2007»
14 years 9 months ago
The stochastic location model with risk pooling
In this paper, we present a stochastic version of the Location Model with Risk Pooling (LMRP) that optimizes location, inventory, and allocation decisions under random parameters ...
Lawrence V. Snyder, Mark S. Daskin, Chung-Piaw Teo