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MCS
2000
Springer
15 years 8 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
SIAMSC
2010
137views more  SIAMSC 2010»
14 years 11 months ago
Accuracy Measures and Fourier Analysis for the Full Multigrid Algorithm
Abstract. The full multigrid (FMG) algorithm is often claimed to achieve so-called discretizationlevel accuracy. In this paper, this notion is formalized by defining a worst-case r...
Carmen Rodrigo, Francisco J. Gaspar, Cornelis W. O...
PAA
2010
14 years 11 months ago
A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion
Abstract Fingerprint classification represents an important preprocessing step in fingerprint identification, which can be very helpful in reducing the cost of searching large fing...
Uday Rajanna, Ali Erol, George Bebis
BMCBI
2005
131views more  BMCBI 2005»
15 years 4 months ago
Regularized Least Squares Cancer Classifiers from DNA microarray data
Background: The advent of the technology of DNA microarrays constitutes an epochal change in the classification and discovery of different types of cancer because the information ...
Nicola Ancona, Rosalia Maglietta, Annarita D'Addab...
DAS
2006
Springer
15 years 8 months ago
Combining Multiple Classifiers for Faster Optical Character Recognition
Traditional approaches to combining classifiers attempt to improve classification accuracy at the cost of increased processing. They may be viewed as providing an accuracy-speed tr...
Kumar Chellapilla, Michael Shilman, Patrice Simard