Prediction and MDL for infinite sequences

IF 0.6 4区 计算机科学 Q4 COMPUTER SCIENCE, THEORY & METHODS
Alexey Milovanov
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引用次数: 0

Abstract

We combine Solomonoff’s approach to universal prediction with algorithmic statistics and suggest to use the computable measure that provides the best “explanation” for the observed data (in the sense of algorithmic statistics) for prediction. In this way we keep the expected sum of squares of prediction errors bounded (as it was for the Solomonoff’s predictor) and, moreover, guarantee that the sum of squares of prediction errors is bounded along any Martin-Löf random sequence. An extended abstract of this paper was presented at the 16th International Computer Science Symposium in Russia (CSR 2021) (Milovanov 2021).

无限序列的预测和 MDL
我们将所罗门诺夫的通用预测方法与算法统计相结合,建议使用能对观测数据提供最佳 "解释"(算法统计意义上的)的可计算度量进行预测。通过这种方法,我们可以保持预测误差的预期平方和有界(就像所罗门诺夫预测器一样),而且还能保证预测误差的平方和沿着任何马丁-洛夫随机序列都有界。本文的扩展摘要已在第 16 届俄罗斯国际计算机科学研讨会(CSR 2021)上发表(Milovanov 2021)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Theory of Computing Systems
Theory of Computing Systems 工程技术-计算机:理论方法
CiteScore
1.90
自引率
0.00%
发文量
36
审稿时长
6-12 weeks
期刊介绍: TOCS is devoted to publishing original research from all areas of theoretical computer science, ranging from foundational areas such as computational complexity, to fundamental areas such as algorithms and data structures, to focused areas such as parallel and distributed algorithms and architectures.
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