Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Batch mode active learning and its application to medical image classification
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning low-rank kernel matrices
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
SimpleNPKL: simple non-parametric kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Learning from pairwise constraints by Similarity Neural Networks
Neural Networks
Online multi-modal distance learning for scalable multimedia retrieval
Proceedings of the sixth ACM international conference on Web search and data mining
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Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information (e.g., either labeled examples or pairwise constraints). However, most previous studies are limited to "passive" kernel learning in which side information is provided beforehand. In this paper we present a framework of Active Kernel Learning (AKL) that actively identifies the most informative pairwise constraints for kernel learning. The key challenge of active kernel learning is how to measure the informativeness of an example pair given its class label is unknown. To this end, we propose a min-max approach for active kernel learning that selects the example pair that results in a large classification margin regardless of its assigned class label. We furthermore approximate the related optimization problem into a convex programming problem. We evaluate the effectiveness of the proposed algorithm by comparing it to two other implementations of active kernel learning. Empirical study with nine datasets on semi-supervised data clustering shows that the proposed algorithm is more effective than its competitors.