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» Evaluating learning algorithms and classifiers
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CVPR
2008
IEEE
16 years 6 months ago
Boosting ordinal features for accurate and fast iris recognition
In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris represen...
Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu,...
121
Voted
KDD
2001
ACM
211views Data Mining» more  KDD 2001»
16 years 4 months ago
Magical thinking in data mining: lessons from CoIL challenge 2000
CoIL challenge 2000 was a supervised learning contest that attracted 43 entries. The authors of 29 entries later wrote explanations of their work. This paper discusses these repor...
Charles Elkan
117
Voted
KDD
2006
ACM
153views Data Mining» more  KDD 2006»
16 years 4 months ago
Model compression
Often the best performing supervised learning models are ensembles of hundreds or thousands of base-level classifiers. Unfortunately, the space required to store this many classif...
Cristian Bucila, Rich Caruana, Alexandru Niculescu...
CRV
2006
IEEE
155views Robotics» more  CRV 2006»
15 years 10 months ago
Collaborative Multi-Camera Surveillance with Automated Person Detection
This paper presents the groundwork for a distributed network of collaborating, intelligent surveillance cameras, implemented with low-cost embedded microprocessor camera modules. ...
Trevor Ahmedali, James J. Clark
ICMLA
2008
15 years 6 months ago
Text Classification Using Tree Kernels and Linguistic Information
Standard Machine Learning approaches to text classification use the bag-of-words representation of documents to deceive the classification target function. Typical linguistic stru...
Teresa Gonçalves, Paulo Quaresma