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- Alexey V. Chernov, Yuri Kalnishkan, Fedor Zhdanov, Vladimir Vovk
- Theor. Comput. Sci.
- 2008

This paper compares two methods of prediction with expert advice, the Aggregating Algorithm and the Defensive Forecasting, in two different settings. The first setting is traditional, with a countable number of experts and a finite number of outcomes. Surprisingly, these two methods of fundamentally different origin lead to identical procedures. In the… (More)

- Vladimir Vovk, Fedor Zhdanov
- Journal of Machine Learning Research
- 2008

We show that the Brier game of prediction is mixable and find the optimal learning rate and substitution function for it. The resulting prediction algorithm is applied to predict results of football and tennis matches. The theoretical performance guarantee turns out to be rather tight on these data sets, especially in the case of the more extensive tennis… (More)

- Alexey V. Chernov, Fedor Zhdanov
- ALT
- 2010

We study prediction with expert advice in the setting where the losses are accumulated with some discounting and the impact of old losses can gradually vanish. We generalize the Aggregating Algorithm and the Aggregating Algorithm for Regression, propose a new variant of exponentially weighted average algorithm, and prove bounds on the cumulative discounted… (More)

- Fedor Zhdanov
- 2011

- Fedor Zhdanov, Yuri Kalnishkan
- ALT
- 2010

This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the properties of the cumulative loss of on-line ridge regression are also obtained.

- Fedor Zhdanov, Vladimir Vovk
- ECML/PKDD
- 2010

- Fedor Zhdanov, Vladimir Vovk
- ArXiv
- 2009

We study the problem of online regression. We do not make any assumptions about input vectors or outcomes. We prove a theoretical bound on the square loss of Ridge Regression. We also show that Bayesian Ridge Regression can be thought of as an online algorithm competing with all the Gaussian linear experts. We then consider the case of infinite-dimensional… (More)

- Fedor Zhdanov, Yuri Kalnishkan
- International Journal on Artificial Intelligence…
- 2012

Multi-class classification is one of the most important tasks in machine learning. In this paper we consider two online multi-class classification problems: classification by a linear model and by a kernelized model. The quality of predictions is measured by the Brier loss function. We obtain two computationally efficient algorithms for these problems by… (More)

- Fedor Zhdanov, Yuri Kalnishkan
- AIAI
- 2010

Multi-class classification is one of the most important tasks in machine learning. In this paper we consider two online multi-class classification problems: classification by a linear model and by a kernelized model. The quality of predictions is measured by the Brier loss function. We suggest two computationally efficient algorithms to work with these… (More)

In this paper we apply computer learning methods to diagnosing ovarian cancer using the level of the standard biomarker CA125 in conjunction with information provided by mass-spectrometry. We are working with a new data set collected over a period of 7 years. Using the level of CA125 and mass-spectrometry peaks, our algorithm gives probability predictions… (More)