Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Query Learning with Large Margin Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Active Learning to Recognize Multiple Types of Plankton
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Inconsistency-based active learning for support vector machines
Pattern Recognition
Active graph matching based on pairwise probabilities between nodes
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Querying discriminative and representative samples for batch mode active learning
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In classification problems, active learning is often adopted to alleviate the laborious human labeling efforts, by finding the most informative samples to query the labels. One of the most popular query strategy is selecting the most uncertain samples for the current classifier. The performance of such an active learning process heavily relies on the learned classifier before each query. Thus, stepwise classifier model/parameter selection is quite critical, which is, however, rarely studied in the literature. In this paper, we propose a novel active learning support vector machine algorithm with adaptive model selection. In this algorithm, before each new query, we trace the full solution path of the base classifier, and then perform efficient model selection using the unlabeled samples. This strategy significantly improves the active learning efficiency with comparatively inexpensive computational cost. Empirical results on both artificial and real world benchmark data sets show the encouraging gains brought by the proposed algorithm in terms of both classification accuracy and computational cost.