Machine Learning
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A discriminative framework for clustering via similarity functions
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
IEEE Transactions on Information Theory
Robust reductions from ranking to classification
COLT'07 Proceedings of the 20th annual conference on Learning theory
Nonparametric statistical inference for ergodic processes
IEEE Transactions on Information Theory
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem
The Journal of Machine Learning Research
Asymptotically optimal classification for multiple tests with empirically observed statistics
IEEE Transactions on Information Theory
Complexity-based induction systems: Comparisons and convergence theorems
IEEE Transactions on Information Theory
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A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d. data. It is shown how this metric can be used for solving statistical problems that are seemingly unrelated to classification and concern highly dependent time series. Specifically, the problems of time-series clustering, homogeneity testing and the three-sample problem are addressed. Universal consistency of the resulting algorithms is proven under most general assumptions. The theoretical results are illustrated with experiments on synthetic and real-world data.