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L. Gyrfi

dblp:03/2918 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Information theory · 100%
Artificial intelligence
1 paper
Learning theory · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information theory › algorithmic information theory
universal prediction		 0.112007
Sequential Prediction of Unbounded Stationary Time Series · IEEE Trans. Inf. Theory 2007
Machine learning › Learning theory › online learning › sequence prediction
individual sequence prediction		 0.012007
Sequential Prediction of Unbounded Stationary Time Series · IEEE Trans. Inf. Theory 2007
Machine learning › Learning theory
online learning		 0.012007
Sequential Prediction of Unbounded Stationary Time Series · IEEE Trans. Inf. Theory 2007

Methods — techniques the papers use, named apart from their topics

predictor combination · 0.1ergodic theory · 0.1
YearPublicationVenuePosition
2007 Sequential Prediction of Unbounded Stationary Time Series
abstract
A simple on-line procedure is considered for the prediction of a real-valued sequence. The algorithm is based on a combination of several simple predictors. If the sequence is a realization of an unbounded stationary and ergodic random process then the average of squared errors converges, almost surely, to that of the optimum, given by the Bayes predictor. An analog result is offered for the classification of binary processes.
L. Gyrfi, G. Ottucsk
 IEEE Trans. Inf. Theory1