Small Sample Discrimination and Professional Performance Assessment

机译：小型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：类重叠和小样本类大小，在实际设置中不常见的情况，可以使分类系统的成功构建非常困难。在本文中，我们将通过一种新的程序来解决这个问题，这是我们称之为非线性判别分析（NLDA）的新程序，用于在这种情况下进行分类器结构，并且将众所周知的多层感知者与无目标经典相结合的优异近似性质渔民分析的鉴别技术。除了NLDA基本面的简短描述之外，我们将在实际问题中说明其使用，对保险销售人员的专业表现进行评估。

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《International work-conference on artificial and natural neural networks》|1999年||共9页
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David Aguado; Jose R.Dorronsoro; Beatriz Lucia; Carlos Santa Cruz;
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中图分类自动化系统理论;
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Small Sample Discrimination and Professional Performance Assessment

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