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ICML
2006
IEEE
14 years 9 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
IIS
2000
13 years 10 months ago
Speeding Up Evolution through Learning: LEM
This paper reports briefly on the development of a new approach to evolutionary computation, called the Learnable Evolution Model or LEM. In contrast to conventional Darwinian-typ...
Ryszard S. Michalski, Guido Cervone, Kenneth A. Ka...
CVPR
2010
IEEE
14 years 5 months ago
The Role of Features, Algorithms and Data in Visual Recognition
There are many computer vision algorithms developed for visual (scene and object) recognition. Some systems focus on involved learning algorithms, some leverage millions of trainin...
Devi Parikh and C. Lawrence Zitnick
EMNLP
2008
13 years 10 months ago
Learning to Predict Code-Switching Points
Predicting possible code-switching points can help develop more accurate methods for automatically processing mixed-language text, such as multilingual language models for speech ...
Thamar Solorio, Yang Liu
ECTEL
2007
Springer
14 years 2 months ago
CAMEL: Taking the Technology Enhanced Learning Journey without Reinventing the Wheel
Projects involving technology are notoriously dogged with difficulties and a number of lessons can be learned. Rather than detail examples from particular TEL projects, the author ...
Gill Ferrell