Noise modelling and evaluating learning from examples
Noise modelling and evaluating learning from examples
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DOI：
10.1016/0004-3702(94)00094-8
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发表时间：
1996-04-01
影响因子：
14.4
通讯作者：
Hickey, RJ
中科院分区：
文献类型：
--
作者：
Hickey, RJ
使用人工数据评估ID3等算法的手段得到了改进，这些算法从实例中学习概念，被称为人工宇宙方法。核心概念是类模型及其相关表示，其中类属性被视为因变量，描述属性被视为自变量。对模型中噪声的性质进行了讨论，并利用信息论的思想，特别是优化论思想对其进行了建模。不相关属性的概念也被考虑在内。这些想法是通过构建一个小宇宙来说明的，然后这个小宇宙被改变以增加噪音。ID3在这些宇宙产生的数据上使用的学习曲线是从试验中估计出来的。这些都表明，增加噪音对学习有不利影响。
The means of evaluating, using artificial data, algorithms, such as ID3, which learn concepts from examples is enhanced and referred to as the method of artificial universes. The central notions are that of a class model and its associated representations in which a class attribute is treated as a dependent variable with description attributes functioning as the independent variables. The nature of noise in the model is discussed and modelled using information-theoretic ideas especially that of majorisation. The notion of an irrelevant attribute is also considered. The ideas are illustrated through the construction of a small universe which is then altered to increase noise. Learning curves for ID3 used on data generated from these universes are estimated from trials. These show that increasing noise has a detrimental effect on learning.