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GECCO
2006
Springer
168views Optimization» more  GECCO 2006»
13 years 11 months ago
A Bayesian approach to learning classifier systems in uncertain environments
In this paper we propose a Bayesian framework for XCS [9], called BXCS. Following [4], we use probability distributions to represent the uncertainty over the classifier estimates ...
Davide Aliprandi, Alex Mancastroppa, Matteo Matteu...
FLAIRS
2008
13 years 10 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
CVPR
2008
IEEE
14 years 10 months ago
Boosting adaptive linear weak classifiers for online learning and tracking
Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak ...
Toufiq Parag, Fatih Porikli, Ahmed M. Elgammal
JASIS
2006
96views more  JASIS 2006»
13 years 8 months ago
Learning to classify documents according to genre
Genre or style analysis can be used to improve results achieved using standard IR techniques. A genre class is a group of documents that are written in a similar style. Genre clas...
Aidan Finn, Nicholas Kushmerick
WWW
2009
ACM
14 years 8 months ago
Purely URL-based topic classification
Given only the URL of a web page, can we identify its topic? This is the question that we examine in this paper. Usually, web pages are classified using their content [7], but a U...
Eda Baykan, Monika Rauch Henzinger, Ludmila Marian...