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» Combining Estimators Using Non-Constant Weighting Functions
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FLAIRS
2000
15 years 3 months ago
Overriding the Experts: A Stacking Method for Combining Marginal Classifiers
The design of an optimal Bayesian classifier for multiple features is dependent on the estimation of multidimensional joint probability density functions and therefore requires a ...
Mark D. Happel, Peter Bock
EOR
2007
159views more  EOR 2007»
15 years 1 months ago
Solving the semi-desirable facility location problem using bi-objective particle swarm
In this paper, a new model for the semi-obnoxious facility location problem is introduced. The new model is composed of a weighted minisum function to represent the transportation...
Haluk Yapicioglu, Alice E. Smith, Gerry V. Dozier
100
Voted
ICRA
1998
IEEE
117views Robotics» more  ICRA 1998»
15 years 6 months ago
Integrating Dependent Sensory Data
In sensory data fusion and integration consideration, sensor independence is a common assumption. In this paper, we demonstrated the impact of including dependent information in s...
Albert C. S. Chung, Helen C. Shen
ICASSP
2010
IEEE
14 years 12 months ago
A transient analysis for the convex combination of two adaptive filters with transfer of coefficients
This paper proposes an improved model for the transient of convex combinations of adaptive filters. A previous model, based on a firstorder Taylor series approximation of the nonl...
Magno T. M. Silva, Vitor H. Nascimento, Jeró...
MCS
2000
Springer
15 years 5 months ago
Analysis of a Fusion Method for Combining Marginal Classifiers
The use of multiple features by a classifier often leads to a reduced probability of error, but the design of an optimal Bayesian classifier for multiple features is dependent on t...
Mark D. Happel, Peter Bock