Training connectionist networks with queries and selective sampling
Advances in neural information processing systems 2
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
ICML '06 Proceedings of the 23rd international conference on Machine learning
Incremental Algorithms for Hierarchical Classification
The Journal of Machine Learning Research
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
Tracking the best hyperplane with a simple budget Perceptron
Machine Learning
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Knows what it knows: a framework for self-aware learning
Proceedings of the 25th international conference on Machine learning
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Proceedings of the 25th international conference on Machine learning
COLT'07 Proceedings of the 20th annual conference on Learning theory
Analysis of perceptron-based active learning
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Learning with stochastic inputs and adversarial outputs
Journal of Computer and System Sciences
Selective sampling on graphs for classification
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Modelling political disaffection from Twitter data
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
Selective sampling and active learning from single and multiple teachers
The Journal of Machine Learning Research
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We introduce a new algorithm for binary classification in the selective sampling protocol. Our algorithm uses Regularized Least Squares (RLS) as base classifier, and for this reason it can be efficiently run in any RKHS. Unlike previous margin-based semi-supervised algorithms, our sampling condition hinges on a simultaneous upper bound on bias and variance of the RLS estimate under a simple linear label noise model. This fact allows us to prove performance bounds that hold for an arbitrary sequence of instances. In particular, we show that our sampling strategy approximates the margin of the Bayes optimal classifier to any desired accuracy ε by asking Õ (d/ε2) queries (in the RKHS case d is replaced by a suitable spectral quantity). While these are the standard rates in the fully supervised i.i.d. case, the best previously known result in our harder setting was Õ (d3/ε4). Preliminary experiments show that some of our algorithms also exhibit a good practical performance.