The nature of statistical learning theory
The nature of statistical learning theory
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Calibration with many checking rules
Mathematics of Operations Research
On the influence of the kernel on the consistency of support vector machines
The Journal of Machine Learning Research
Prediction, Learning, and Games
Prediction, Learning, and Games
Non-asymptotic calibration and resolution
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Defensive prediction with expert advice
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Predictions as statements and decisions
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Function classes that approximate the bayes risk
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Calibration and internal no-regret with random signals
ALT'09 Proceedings of the 20th international conference on Algorithmic learning theory
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We analyze a new algorithm for probability forecasting of binary observations on the basis of the available data, without making any assumptions about the way the observations are generated. The algorithm is shown to be well-calibrated and to have good resolution for long enough sequences of observations and for a suitable choice of its parameter, a kernel on the Cartesian product of the forecast space [0, 1] and the data space. Our main results are non-asymptotic: we establish explicit inequalities, shown to be tight, for the performance of the algorithm.