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» Randomness, Stochasticity and Approximations
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SMA
1993
ACM
107views Solid Modeling» more  SMA 1993»
15 years 10 months ago
Relaxed parametric design with probabilistic constraints
: Parametric design is an important modeling paradigm in computer aided design. Relationships (constraints) between the degrees of freedom (DOFs) of the model, instead of the DOFs ...
Yacov Hel-Or, Ari Rappoport, Michael Werman
ALT
2010
Springer
15 years 7 months ago
Online Multiple Kernel Learning: Algorithms and Mistake Bounds
Online learning and kernel learning are two active research topics in machine learning. Although each of them has been studied extensively, there is a limited effort in addressing ...
Rong Jin, Steven C. H. Hoi, Tianbao Yang
WSC
1998
15 years 7 months ago
Comparison of Bayesian and Frequentist Assessments of Uncertainty for Selecting the Best System
An important problem in discrete-event stochastic simulation is the selection of the best system from a finite set of alternatives. There are many techniques for ranking and selec...
Koichiro Inoue, Stephen E. Chick
WSC
1997
15 years 7 months ago
Searching for Important Factors: Sequential Bifurcation under Uncertainty
The problem of searching for important factors in a simulation model is considered when the simulation output is subject to stochastic variation. Bettonvil and Kleijnen (1996) giv...
Russell C. H. Cheng
ICGA
2006
83views Optimization» more  ICGA 2006»
15 years 6 months ago
A Tool for the Direct Assessment of Poker Decisions
The element of luck permeates every aspect of the game of poker. Random stochastic outcomes introduce a large amount of noise that can make it very difficult to distinguish a good...
Darse Billings, Morgan Kan