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118
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ICPR
2010
IEEE
15 years 5 months ago
Fast Training of Object Detection Using Stochastic Gradient Descent
Training datasets for object detection problems are typically very large and Support Vector Machine (SVM) implementations are computationally complex. As opposed to these complex ...
Rob Wijnhoven, Peter H. N. De With
148
Voted
ESA
2006
Springer
139views Algorithms» more  ESA 2006»
15 years 6 months ago
Stochastic Shortest Paths Via Quasi-convex Maximization
Abstract. We consider the problem of finding shortest paths in a graph with independent randomly distributed edge lengths. Our goal is to maximize the probability that the path len...
Evdokia Nikolova, Jonathan A. Kelner, Matthew Bran...
128
Voted
MOBICOM
2006
ACM
15 years 8 months ago
Stochastic event capture using mobile sensors subject to a quality metric
Mobile sensors cover more area over a period of time than the same number of stationary sensors. However, the quality of coverage achieved by mobile sensors depends on the velocit...
Nabhendra Bisnik, Alhussein A. Abouzeid, Volkan Is...
79
Voted
EUSFLAT
2003
121views Fuzzy Logic» more  EUSFLAT 2003»
15 years 3 months ago
An adaptive learning algorithm for a neo fuzzy neuron
In the paper, a new optimal learning algorithm for a neo-fuzzy neuron (NFN) is proposed. The algorithm is characteristic in that it provides online tuning of not only the synaptic...
Yevgeniy Bodyanskiy, Illya Kokshenev, Vitaliy Kolo...
FOCS
2004
IEEE
15 years 6 months ago
Approximating the Stochastic Knapsack Problem: The Benefit of Adaptivity
We consider a stochastic variant of the NP-hard 0/1 knapsack problem in which item values are deterministic and item sizes are independent random variables with known, arbitrary d...
Brian C. Dean, Michel X. Goemans, Jan Vondrá...