基于近似度的插值:一种新的插值方法

Shiyou Lian
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引用次数: 0

摘要

本文介绍了数值间近似度数的度量和近似度数函数的概念,从而提出了一种新的插值方法——基于近似度数的插值,即AD插值。利用相关插值公式直接进行一维AD插值;首先将n(n>1)维AD插补分离成n个并行的一维AD插补计算,分别进行计算,然后将计算结果通过和时差公式综合成一个值作为n维插补的结果值。如果采用并行处理,则n维AD插补的效率与一维AD插补的效率几乎相同。由此开辟了一条可行、便捷的途径,为高维插值提供了一种有效的方法。此外,如果将AD插值引入到机器学习中,有望实现一种新的基于实例的学习方法。
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Approximation-Degree-Based Interpolation: A New Interpolation Method
This paper introduces the measure of approximate-degree and the concept of approximate-degree function between numerical values, thus developing a new interpolation method —— approximation-degree-based interpolation, i.e., AD interpolation. One-dimensional AD interpolation is done directly by using correlative interpolation formulas; n(n>1)-dimensional AD interpolation is firstly separated into n parallel one-dimensional AD interpolation computations to do respectively, and then got results are synthesized by Sum-Times-Difference formula into a value as the result value of the n-dimensional interpolation. If the parallel processing is used, the efficiency of n-dimensional AD interpolation is almost the same as that of the one-dimensional AD interpolation. Thus it starts a feasible and convenient approach and provides an effective method for high-dimensional interpolations. Furthermore, if AD interpolation is introduced into machine learning, a new instance-based learning method is expected to be realized.
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