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IMPACT OF LEVEL OF DETAIL AND INFORMATION CONTENT ON ACCURACY OF FUNCTION STRUCTURE-BASED MARKET PRICE PREDICTION MODELS

机译:细节水平和信息内容水平对基于功能结构的准确性的影响

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This paper explores the amount of information stored in the representational components of a function structure: vocabulary, grammar, and topology. This is done by classifying the previously developed functional composition rules into vocabulary, grammatical, and topological classes and applying them to function structures available in an external design repository. The pruned function structures of electromechanical devices are then evaluated for how accurately market values can be predicted using graph complexity connectivity method. The accuracy is inversely with amount of information and level of detail. Applying the topological rule does not significantly impact the predictive power of the models, while applying the vocabulary rules and the grammar rules reduce the accuracy of the predictions. Finally, the least predictive model set is that which had all rules applied. In this manner, the value of a representation to predict or answer questions is quantified through this research approach.
机译:本文探讨了存储在函数结构的代表组件中的信息量:词汇,语法和拓扑。这是通过将先前开发的功能组成规则分类为词汇,语法和拓扑类,并将它们应用于外部设计存储库中可用的功能结构来完成。然后评估机电设备的修剪功能结构,以使用图形复杂性连接方法预测市场值的准确性。准确性与信息量和细节水平相反。应用拓扑规则不会显着影响模型的预测力,同时应用词汇规则,语法规则降低了预测的准确性。最后,最少的预测模型集是所有规则所应用的。以这种方式,通过该研究方法量化以预测或回答问题的表示的值。

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