Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Explicit learning curves for transduction and application to clustering and compression algorithms
Journal of Artificial Intelligence Research
Almost-everywhere algorithmic stability and generalization error
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Transductive link spam detection
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Spectral clustering and transductive learning with multiple views
Proceedings of the 24th international conference on Machine learning
Non-stationary data sequence classification using online class priors estimation
Pattern Recognition
Stability of transductive regression algorithms
Proceedings of the 25th international conference on Machine learning
Error bounds of multi-graph regularized semi-supervised classification
Information Sciences: an International Journal
Transductive Rademacher complexity and its applications
Journal of Artificial Intelligence Research
Transductive rademacher complexity and its applications
COLT'07 Proceedings of the 20th annual conference on Learning theory
Transfer estimation of evolving class priors in data stream classification
Pattern Recognition
Query-biased learning to rank for real-time twitter search
Proceedings of the 21st ACM international conference on Information and knowledge management
A survey of learning to rank for real-time twitter search
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Clustering-based transduction for learning a ranking model with limited human labels
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of transductive stability, which measures the sensitivity of the algorithm to most pairwise exchanges of training and test set points. Our bound is based on a novel concentration inequality for symmetric functions of permutations. We also present a simple sampling technique that can estimate, with high probability, the weak stability of transductive learning algorithms with respect to a given dataset. We demonstrate the usefulness of our estimation technique on a well known transductive learning algorithm.