Sciweavers

1148 search results - page 5 / 230
» Methods for Dynamic Classifier Selection
Sort
View
ICML
2002
IEEE
14 years 8 months ago
Is Combining Classifiers Better than Selecting the Best One
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Saso Dzeroski, Bernard Zenko
DMIN
2006
158views Data Mining» more  DMIN 2006»
13 years 9 months ago
Ensemble Selection Using Diversity Networks
- An ideal ensemble is composed of base classifiers that perform well and that have minimal overlap in their errors. Eliminating classifiers from an ensemble based on a criterion t...
Qiang Ye, Paul W. Munro
FLAIRS
2000
13 years 9 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
ICMCS
2008
IEEE
174views Multimedia» more  ICMCS 2008»
14 years 2 months ago
Cascaded classification with optimal candidate selection for effective place recognition
A two-stage cascaded classification approach with an optimal candidate selection scheme is proposed to recognize places using images taken by camera phones. An optimal acceptance ...
Yiqun Li, Joo-Hwee Lim, Hanlin Goh
IDA
2003
Springer
14 years 25 days ago
A Semi-supervised Method for Learning the Structure of Robot Environment Interactions
For a mobile robot to act autonomously, it must be able to construct a model of its interaction with the environment. Oates et al. developed an unsupervised learning method that pr...
Axel Großmann, Matthias Wendt, Jeremy Wyatt