Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to remove Internet advertisements
Proceedings of the third annual conference on Autonomous Agents
Machine Learning
Machine Learning
Selective Sampling with Redundant Views
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Active learning: theory and applications
Active learning: theory and applications
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian processes for canonical correlation analysis
Neurocomputing
High Reliable Multi-View Semi-Supervised Learning with Extremely Sparse Labeled Data
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Active learning with statistical models
Journal of Artificial Intelligence Research
Semi-Supervised Learning
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Semi-supervised multitask learning via self-training and maximum entropy discrimination
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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In the setting of active learning there exists a general assumption that labeled examples are available for training a classifier, which in turn is used to examine unlabeled data to select the most 'informative' examples for manual labeling. However, in some domain applications there are a limited number of labeled examples available, such as in the most extreme cases of having a single labeled example per category. In these scenarios, the most existing active learning methodologies cannot be directly applied without initially making an assumption on label assignment. In this paper we present a method for finding high-informative examples for manual labeling based on extremely limited labeled data available during training. We propose using canonical correlation analysis to investigate the correlation between different views of the available data and demonstrate that this measure can be used as a selection criterion for the novel application of active learning using only a single labeled example from each class. We demonstrate our method with promising experimental results on text classification, advertisement removal and multi-class image classification tasks.