Supervised Learning from Clustered Input Examples
Supervised Learning from Clustered Input Examples
复制标题
从集群输入示例进行监督学习
DOI：
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发表时间：
1995
期刊：
影响因子：
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通讯作者：
S. Solla
中科院分区：
文献类型：
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作者：
C. Marangi;Michael Biehl;S. Solla
在本文中，我们分析了在输入分布中引入结构对简单感知器泛化能力的影响。考虑两个输入数据簇和线性可分离规则的简单情况。我们发现泛化能力随着簇之间的分离而提高，并且从下面受到非结构化情况的结果的限制，随着簇之间的分离消失而恢复。然而，大型训练集的渐近行为对于结构化和非结构化输入分布是相同的。对于小型训练集，对于模型参数的某些值，泛化误差对示例数量的依赖性是非单调的。
In this paper we analyse the effect of introducing a structure in the input distribution on the generalization ability of a simple perceptron. The simple case of two clusters of input data and a linearly separable rule is considered. We find that the generalization ability improves with the separation between the clusters, and is bounded from below by the result for the unstructured case, recovered as the separation between clusters vanishes. The asymptotic behaviour for large training sets, however, is the same for structured and unstructured input distributions. For small training sets, the dependence of the generalization error on the number of examples is observed to be non-monotonic for certain values of the model parameters.