Online Regression Competitive with Changing Predictors
Online Regression Competitive with Changing Predictors
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DOI：
10.1007/978-3-540-75225-7_17
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发表时间：
2007-10
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影响因子：
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通讯作者：
Steven Busuttil;Yuri Kalnishkan
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文献类型：
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作者：
Steven Busuttil;Yuri Kalnishkan
本文讨论了在线学习模式下的预测问题，其中输出对信号的依赖性可以随时间而变化。聚合算法（AA）是一种技术，它最佳地合并来自池中的专家，因此产生的策略遭受的累积损失几乎与池中最好的专家一样好。我们应用AA的情况下，专家都是线性预测，可以随时间变化。KAARCh是最终算法的内核版本。在核的情况下，专家是在一些再生核希尔伯特空间中的所有决策规则，可以随时间变化。我们表明，KAARCH遭受的累积平方损失，几乎是一样好的任何专家，不改变非常迅速。
This paper deals with the problem of making predictions in the online mode of learning where the dependence of the outcomeyton the signal xtcan change with time. The Aggregating Algorithm (AA) is a technique that optimally merges experts from a pool, so that the resulting strategy suffers a cumulative loss that is almost as good as that of the best expert in the pool. We apply the AA to the case where the experts are all the linear predictors that can change with time. KAARCh is the kernel version of the resulting algorithm. In the kernel case, the experts are all the decision rules in some reproducing kernel Hilbert space that can change over time. We show that KAARCh suffers a cumulative square loss that is almost as good as that of any expert that does not change very rapidly.