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» A Technique to Classify and Compare Agile Methods
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KDD
2006
ACM
118views Data Mining» more  KDD 2006»
15 years 10 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
85
Voted
CEC
2009
IEEE
15 years 25 days ago
Using genetic programming to obtain implicit diversity
—When performing predictive data mining, the use of ensembles is known to increase prediction accuracy, compared to single models. To obtain this higher accuracy, ensembles shoul...
Ulf Johansson, Cecilia Sönströd, Tuve L&...
CVPR
2008
IEEE
15 years 11 months ago
Mining compositional features for boosting
The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the t...
Junsong Yuan, Jiebo Luo, Ying Wu
KDD
2001
ACM
216views Data Mining» more  KDD 2001»
15 years 10 months ago
The distributed boosting algorithm
In this paper, we propose a general framework for distributed boosting intended for efficient integrating specialized classifiers learned over very large and distributed homogeneo...
Aleksandar Lazarevic, Zoran Obradovic
ICDAR
2009
IEEE
15 years 4 months ago
A Dual Taxonomy for Defects in Digitized Historical Photos
Old photos may be affected by several types of defects. Manual restorers use their own taxonomy to classify damages by which a photo is affected, in order to apply the proper rest...
Edoardo Ardizzone, A. De Polo, Haris Dindo, Giusep...