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» Evaluating learning algorithms and classifiers
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ICML
2006
IEEE
16 years 5 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
FOCS
1999
IEEE
15 years 8 months ago
An Algorithmic Theory of Learning: Robust Concepts and Random Projection
We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that e...
Rosa I. Arriaga, Santosh Vempala
MCS
2007
Springer
15 years 10 months ago
Classifier Combining Rules Under Independence Assumptions
Classifier combining rules are designed for the fusion of the results from the component classifiers in a multiple classifier system. In this paper, we firstly propose a theoretica...
Shoushan Li, Chengqing Zong
WWW
2009
ACM
16 years 5 months ago
A class-feature-centroid classifier for text categorization
Automated text categorization is an important technique for many web applications, such as document indexing, document filtering, and cataloging web resources. Many different appr...
Hu Guan, Jingyu Zhou, Minyi Guo
CVPR
2004
IEEE
16 years 6 months ago
Learning Distance Functions for Image Retrieval
Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary ...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall