Online learning with universal model and predictor classes

被引:0
|
作者
Poland, Jan [1 ]
机构
[1] Hokkaido Univ, Grad Sch Informat Sci & Technol, Sapporo, Hokkaido 060, Japan
来源
2006 IEEE INFORMATION THEORY WORKSHOP | 2006年
关键词
D O I
10.1109/ITW.2006.1633819
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
We review and relate some classical and recent results from the theory of online learning based on discrete classes of models or predictors. Among these frameworks, Bayesian methods, MDL, and prediction (or action) with expert advice are studied. We will discuss ways to work with universal base classes corresponding to sets of all programs on some fixed universal Turing machine, resulting in universal induction schemes.
引用
收藏
页码:237 / 241
页数:5
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