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NAACL
2010
13 years 5 months ago
Training Paradigms for Correcting Errors in Grammar and Usage
This paper proposes a novel approach to the problem of training classifiers to detect and correct grammar and usage errors in text by selectively introducing mistakes into the tra...
Alla Rozovskaya, Dan Roth
ICPR
2006
IEEE
14 years 8 months ago
A New Data Selection Principle for Semi-Supervised Incremental Learning
Current semi-supervised incremental learning approaches select unlabeled examples with predicted high confidence for model re-training. We show that for many applications this dat...
Alexander I. Rudnicky, Rong Zhang
ICIP
2003
IEEE
14 years 21 days ago
Image classification using multimedia knowledge networks
This paper presents novel methods for classifying images based on knowledge discovered from annotated images using WordNet. The novelty of this work is the automatic class discove...
Ana B. Benitez, Shih-Fu Chang
ICST
2008
IEEE
14 years 1 months ago
On Combining Multi-formalism Knowledge to Select Models for Model Transformation Testing
Testing remains a major challenge for model transformation development. Test models that are used as test data for model transformations, are constrained by various sources of kno...
Sagar Sen, Benoit Baudry, Jean-Marie Mottu
ICIAR
2004
Springer
14 years 23 days ago
Learning an Information Theoretic Transform for Object Detection
We present an information theoretic approach for learning a linear dimension reduction transform for object classification. The theoretic guidance of the approach is that the trans...
Jianzhong Fang, Guoping Qiu