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Small Sample Discrimination and Professional Performance Assessment

机译:小样本歧视和专业绩效评估

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Class overlapping and small sample class sizes, a situation not infrequent in practical settings, can make very difficult the successful construction of a classification system. In this paper we will address this question by means of a new procedure, which we call Nonlinear Discriminant Analysis (NLDA), for classifier construction in such cases and that combines the excellent approximation properties of the well known Multilayer Perceptrons with the target-free classical discrimination technique of Fisher's Analysis. Besides a short description of NLDA fundamentals, we will give an illustration of its use in a practical problem, the assessment ofprofessional performance of insurance salespersons.
机译:类别重叠和样本类别小,这在实际环境中并不罕见,这可能会很难成功构建分类系统。在本文中,我们将通过一种称为非线性判别分析(NLDA)的新程序来解决此问题,该程序用于此类情况下的分类器构造,并将众所周知的多层感知器的出色逼近特性与无目标经典算法结合在一起Fisher分析的判别技术。除了对NLDA基本原理的简短描述之外,我们还将举例说明NLDA在实际问题中的使用,即评估保险销售人员的专业绩效。

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