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» Robust Boosting for Learning from Few Examples
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ICCV
2007
IEEE
15 years 11 months ago
Enabling Users to Guide the Design of Robust Model Fitting Algorithms
Model-based image interpretation extracts high-level information from images using a priori knowledge about the object of interest. The computational challenge in model fitting is...
Matthias Wimmer, Freek Stulp, Bernd Radig
MLMI
2007
Springer
15 years 3 months ago
Automatic Labeling Inconsistencies Detection and Correction for Sentence Unit Segmentation in Conversational Speech
In conversational speech, irregularities in the speech such as overlaps and disruptions make it difficult to decide what is a sentence. Thus, despite very precise guidelines on how...
Sébastien Cuendet, Dilek Z. Hakkani-Tü...
SIGIR
2010
ACM
15 years 1 months ago
Combining coregularization and consensus-based self-training for multilingual text categorization
We investigate the problem of learning document classifiers in a multilingual setting, from collections where labels are only partially available. We address this problem in the ...
Massih-Reza Amini, Cyril Goutte, Nicolas Usunier
CEAS
2007
Springer
15 years 1 months ago
Online Active Learning Methods for Fast Label-Efficient Spam Filtering
Active learning methods seek to reduce the number of labeled examples needed to train an effective classifier, and have natural appeal in spam filtering applications where trustwo...
D. Sculley
CLOR
2006
15 years 1 months ago
Shared Features for Multiclass Object Detection
Abstract. We consider the problem of detecting a large number of different classes of objects in cluttered scenes. We present a learning procedure, based on boosted decision stumps...
Antonio B. Torralba, Kevin P. Murphy, William T. F...