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FLAIRS
2008
13 years 9 months ago
Building Useful Models from Imbalanced Data with Sampling and Boosting
Building useful classification models can be a challenging endeavor, especially when training data is imbalanced. Class imbalance presents a problem when traditional classificatio...
Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van H...
RSS
2007
136views Robotics» more  RSS 2007»
13 years 9 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...
CGF
2006
126views more  CGF 2006»
13 years 7 months ago
Soft Shadow Maps: Efficient Sampling of Light Source Visibility
Shadows, particularly soft shadows, play an important role in the visual perception of a scene by providing visual cues about the shape and position of objects. Several recent alg...
Lionel Atty, Nicolas Holzschuch, Marc Lapierre, Je...
ISPASS
2006
IEEE
14 years 1 months ago
Simulation sampling with live-points
Current simulation-sampling techniques construct accurate model state for each measurement by continuously warming large microarchitectural structures (e.g., caches and the branch...
Thomas F. Wenisch, Roland E. Wunderlich, Babak Fal...
ESA
2010
Springer
246views Algorithms» more  ESA 2010»
13 years 8 months ago
Estimating the Average of a Lipschitz-Continuous Function from One Sample
We study the problem of estimating the average of a Lipschitz continuous function f defined over a metric space, by querying f at only a single point. More specifically, we explore...
Abhimanyu Das, David Kempe