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» Is Combining Classifiers Better than Selecting the Best One
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ICCCN
2007
IEEE
13 years 7 months ago
Online Selection of Tracking Features using AdaBoost
In this paper, a novel feature selection algorithm for object tracking is proposed. This algorithm performs more robust than the previous works by taking the correlation between f...
Ying-Jia Yeh, Chiou-Ting Hsu
AIPRF
2007
13 years 9 months ago
Evaluation of Different Approaches to Training a Genre Classifier
This paper presents experiments on classifying web pages by genre. Firstly, a corpus of 1539 manually labeled web pages was prepared. Secondly, 502 genre features were selected ba...
Vedrana Vidulin, Mitja Lustrek, Matjaz Gams
IJON
2008
116views more  IJON 2008»
13 years 7 months ago
Evolutionary ensemble of diverse artificial neural networks using speciation
Recently, many researchers have designed neural network architectures with evolutionary algorithms but most of them have used only the fittest solution of the last generation. To ...
Kyung-Joong Kim, Sung-Bae Cho
PRL
2008
213views more  PRL 2008»
13 years 7 months ago
Boosting recombined weak classifiers
Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the comb...
Juan José Rodríguez, Jesús Ma...
BMCBI
2008
106views more  BMCBI 2008»
13 years 7 months ago
A machine vision system for automated non-invasive assessment of cell viability via dark field microscopy, wavelet feature selec
Background: Cell viability is one of the basic properties indicating the physiological state of the cell, thus, it has long been one of the major considerations in biotechnologica...
Ning Wei, Erwin Flaschel, Karl Friehs, Tim W. Natt...