论文标题
自动化经过梯度下降训练的专家系统网络的设计和开发
Automating the Design and Development of Gradient Descent Trained Expert System Networks
论文作者
论文摘要
先前的工作引入了一个经过梯度下降训练的专家系统，该系统从概念上将神经网络的学习能力与专家系统的可理解性和可辩护逻辑相结合。该系统被证明能够从数据中学习模式，并以与神经网络系统报道的级别相媲美的水平进行决策。但是，该方法的主要限制是进行规则事实网络的手动开发（然后是使用反向传播训练）的必要性。与神经网络相比，本文提出了一种克服这一重大限制的技术。具体而言，本文提出了使用较大且密集的申请需要的规则 - 事实网络，该网络经过训练，修剪，手动审查，然后重新培训供使用。在多个操作条件下评估了多种类型的网络，并对这些结果进行了评估。基于这些单独的实验条件评估，评估了所提出的技术。提供的数据表明，错误率低至3.9％（平均值，中位数1.2％），这证明了该技术对许多应用的疗效。
Prior work introduced a gradient descent trained expert system that conceptually combines the learning capabilities of neural networks with the understandability and defensible logic of an expert system. This system was shown to be able to learn patterns from data and to perform decision-making at levels rivaling those reported by neural network systems. The principal limitation of the approach, though, was the necessity for the manual development of a rule-fact network (which is then trained using backpropagation). This paper proposes a technique for overcoming this significant limitation, as compared to neural networks. Specifically, this paper proposes the use of larger and denser-than-application need rule-fact networks which are trained, pruned, manually reviewed and then re-trained for use. Multiple types of networks are evaluated under multiple operating conditions and these results are presented and assessed. Based on these individual experimental condition assessments, the proposed technique is evaluated. The data presented shows that error rates as low as 3.9% (mean, 1.2% median) can be obtained, demonstrating the efficacy of this technique for many applications.
