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Analysis of Algebraic Expressions Derived from Genetic Multivariate Polynomials and Support Vector Machines: A Case Study

机译:遗传多元多项式和支持载体机的代数表达分析 - 以案例研究

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摘要

We discuss how algebraic explicit expressions modeling a complex phenomenon via an adequate set of data can be derived from the application of Genetic Multivariate Polynomials (GMPs), on the one hand, and Support Vector Machines (SVMs) on the other. A polynomial expression is derived in GMPs in a natural way, whereas in SVMs a polynomial kernel is employed to derive a similar one. In any particular problem an evolutionary determined sample of monomials is required in GMP expressions while, on the other hand, there is a large number of monomials implicit in the SVM approach. We make some experiments to compare the modeling characterization and accuracy obtained from the application of both methods.
机译:我们讨论如何通过足够的数据集建模复杂现象的代数显式表达式可以从一方面的应用程序源自遗传多变量多项式(GMP),并支持另一方面的向量机(SVM)。多项式表达以自然的方式衍生在GMP中,而在SVMS中,使用多项式内核来得出类似的。在任何特定问题中,在GMP表达中需要一种进化确定的单体样品,而另一方面,在SVM方法中隐含大量单体单体。我们进行了一些实验,以比较来自两种方法的应用所获得的建模表征和准确性。

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