Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth 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
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
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Since SVMs have met with significant success in numerous real-world learning, SVM-based active learning has been proposed in the active learning context and it has been successfully applied in the domains like document classification, in which SVMs using linear kernel are known to be effective for the task. However, it is difficult to apply SVM-based active learning to general domains because the kernel used in SVMs should be selected properly before the active learning process but good kernels for the target task is usually unknown. If the pre-selected kernel is inadequate for the target data, both the active learning process and the learned SVM have poor performance. Therefore, new active learning methods are required which effectively find an adequate kernel for the target data as well as the labels of unknown samples in the active learning process. In this paper, we propose a two-phased SKM-based active learning method for the purpose. By experiments, we show that the proposed SKM-based active learning method has quick response suited to interaction with human experts and can find an appropriate kernel among linear combinations of given multiple kernels.