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FLAIRS
2008
15 years 7 months ago
Evolutionary Learning of Dynamic Naive Bayesian Classifiers
Naive Bayesian classifiers work well in data sets with independent attributes. However, they perform poorly when the attributes are dependent or when there are one or more irrelev...
Miguel A. Palacios-Alonso, Carlos A. Brizuela, Lui...
BCSHCI
2008
15 years 6 months ago
Efficiency of multimodal metaphors in the presentation of learning information
The comparative study described in this paper has been conducted to investigate the effect of including multimodal metaphors on the usability of e-learning interfaces. Two indepen...
Marwan Alseid, Dimitrios Rigas
119
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DGO
2010
119views Education» more  DGO 2010»
15 years 6 months ago
Information and transparency: learning from recovery act reporting experiences
The American Recovery and Reinvestment Act (2009) promised strict accounting of all funds spent and the publication of that information to the public in relative real-time. The fe...
Natalie Helbig, Evgeny Styrin, Donna S. Canestraro...
122
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IWPT
2001
15 years 6 months ago
Parsing the CHILDES Database: Methodology and Lessons Learned
This paper discusses the process of parsing adult utterances directed to a child, in an effort to produce a syntactically annotated corpus of the verbal input to a human language ...
Kenji Sagae, Alon Lavie, Brian MacWhinney
AAAI
1998
15 years 6 months ago
Boosting in the Limit: Maximizing the Margin of Learned Ensembles
The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
Adam J. Grove, Dale Schuurmans