Applied statistics: a first course
Applied statistics: a first course
Making large-scale support vector machine learning practical
Advances in kernel methods
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning on the border: active learning in imbalanced data classification
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Reducing class imbalance during active learning for named entity annotation
Proceedings of the fifth international conference on Knowledge capture
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Bucking the trend: large-scale cost-focused active learning for statistical machine translation
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Using Mechanical Turk to build machine translation evaluation sets
CSLDAMT '10 Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk
Inactive learning?: difficulties employing active learning in practice
ACM SIGKDD Explorations Newsletter
A new probabilistic active sample selection algorithm for class imbalance problem
International Journal of Knowledge Engineering and Soft Data Paradigms
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Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing imbalance is to base cost models during AL on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the labeled training data, which is the leading method used during PL.