Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Confidence estimation for information extraction
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
Semi-supervised learning for automatic prosodic event detection using co-training algorithm
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Traffic sign classifier adaption by semi-supervised co-training
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
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Traffic signs in Western European countries share many similarities but also can vary in colour, size, and depicted symbols. Statistical pattern classification methods are used for the automatic recognition of traffic signs in state-of-the-art driver assistance systems. Training a classifier separately for each country requires a huge amount of training data labelled by human annotators. In order to reduce these efforts, a self-learning approach extends the recognition capability of an initial German classifier to other European countries. After the most informative samples have been selected by the confidence band method from a given pool of unlabelled traffic signs, the classifier assigns labels to them. Furthermore, the performance of the self-learning classifier is improved by incorporating synthetically generated samples into the self-learning process. The achieved classification rates are comparable to those of classifiers trained with fully labelled samples.