Towards context-aware personalization and a broad perspective on the semantics of news articles
Proceedings of the fourth ACM conference on Recommender systems
Active supervised domain adaptation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Understanding the risk factors of learning in adversarial environments
Proceedings of the 4th ACM workshop on Security and artificial intelligence
Sub-sampling: Real-time vision for micro air vehicles
Robotics and Autonomous Systems
The Journal of Machine Learning Research
Active learning via perfect selective classification
The Journal of Machine Learning Research
Query strategies for evading convex-inducing classifiers
The Journal of Machine Learning Research
Activized learning: transforming passive to active with improved label complexity
The Journal of Machine Learning Research
A theory of transfer learning with applications to active learning
Machine Learning
Cost-sensitive online active learning with application to malicious URL detection
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback-driven multiclass active learning for data streams
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We start by showing that in an active learning setting, the Perceptron algorithm needs Ω(1/ε2) labels to learn linear separators within generalization error ε. We then present a simple active learning algorithm for this problem, which combines a modification of the Perceptron update with an adaptive filtering rule for deciding which points to query. For data distributed uniformly over the unit sphere, we show that our algorithm reaches generalization error ε after asking for just Õ(d log 1/ε) labels. This exponential improvement over the usual sample complexity of supervised learning had previously been demonstrated only for the computationally more complex query-by-committee algorithm.