Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
The nature of statistical learning theory
The nature of statistical learning theory
A study of support vectors on model independent example selection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Active learning is a generic approach to accelerate training of classifiers in order to achieve a higher accuracy with a small number of training examples. In the past, simple active learning algorithms like random learning and query learning have been proposed for the design of support vector machine (SVM) classifiers. In random learning, examples are chosen randomly, while in query learning examples closer to the current separating hyperplane are chosen at each learning step. However, it is observed that a better scheme would be to use random learning in the initial stages (more exploration) and query learning in the final stages (more exploitation) of learning. Here we present two novel active SV learning algorithms which use adaptive mixtures of random and query learning. One of the proposed algorithms is inspired by online decision problems, and involves a hard choice among the pure strategies at each step. The other extends this to soft choices using a mixture of instances recommended by the individual pure strategies. Both strategies handle the exploration-exploitation trade-off in an efficient manner. The efficacy of the algorithms is demonstrated by experiments on benchmark datasets.