Rule Improvement Through Decision Boundary Detection Using Sensitivity Analysis

机译：通过使用敏感性分析来通过决策边界检测来改进

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Rule extraction from artificial neural networks (ANN) provides a mechanism to interpret the knowledge embedded in the numerical weights. Classification problems with continuous-valued parameters create difficulties in determining boundary conditions for these parameters. This paper presents an approach to locate such boundaries using sensitivity analysis. Inclusion of this decision boundary detection approach in a rule extraction algorithm resulted in significant improvements in rule accuracies.

机译：从人工神经网络（ANN）中提取提供了一种解释嵌入数值权重的知识的机制。连续值参数的分类问题在确定这些参数的边界条件时产生困难。本文介绍了使用灵敏度分析定位此类边界的方法。在规则提取算法中包含该决策边界检测方法导致规则精度的显着改进。

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《International work-conference on artificial and natural neural networks》|1999年||共7页
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AP Engelbrecht; HL Viktor;
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中图分类自动化系统理论;
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7. Rule Improvement Through Decision Boundary Detection Using Sensitivity Analysis [O] . Ap Engelbrecht, HL Viktor 1999

机译：利用灵敏度分析通过决策边界检测改进规则

Rule Improvement Through Decision Boundary Detection Using Sensitivity Analysis

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