A Theory of Cross-Validation Error
نویسنده
چکیده
This paper presents a theory of error in cross-validation testing of algorithms for predicting real-valued attributes. The theory justifies the claim that predicting real-valued attributes requires balancing the conflicting demands of simplicity and accuracy. Furthermore , the theory indicates precisely how these conflicting demands must be balanced, in order to minimize cross-validation error. A general theory is presented, then it is developed in detail for linear regression and instance-based learning.
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عنوان ژورنال:
- J. Exp. Theor. Artif. Intell.
دوره 6 شماره
صفحات -
تاریخ انتشار 1994