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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
116
Voted
EOR
2007
159views more  EOR 2007»
15 years 2 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
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
128
Voted
ICASSP
2010
IEEE
15 years 17 days 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...
152
Voted
MCS
2000
Springer
15 years 6 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