基于类概率输出网络的专利属性预测

W. Park
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引用次数: 1

摘要

维护期限,即从专利注册到专利期满的时间范围,是用来评价专利的一项重要属性。年代质量。为了预测专利的维持期,需要一个一致的分类器。分类器的输出通常由判别函数的值决定,并根据该输出做出决策,而该输出不一定代表分类软决策的后验概率。因此,期望分类器的输出以这样一种方式校准,以包括类隶属度的后验概率。为此,设计了类概率输出网络(CPON)。为分类器的概率缩放提供了一种新的后处理方法。年代输出。为了预测专利的维护周期,在仿真中使用了SVM、KLR、感知器和带CPON的感知器。与SVM、KLR和感知机相比,使用CPON的感知机的仿真结果显示了统计上有意义的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of a Patent Property Using the Class Probability Output Network
The maintenance period, the time frame begging from the registration to the expiration of a patent, is an important property used to evaluate the patent???s quality. To predict the maintenance period of a patent, a consistent classifier is desirable. The output of a classifier is usually determined by the value of a discriminant function and a decision is made based on this output which does not necessarily represent the posterior probability of the soft decision of classification. Thus, it is desirable that the output of a classifier be calibrated in such a way to include the posterior probability of class membership. For this purpose, the Class Probability Output Network (CPON) is devised. It is a new method of postprocessing for the probabilistic scaling of classifier???s output. To predict the maintenance period of a patent, SVM, KLR, perceptron and perceptron with CPON are used in simulation. The simulation results using the perceptron with CPON demonstrated a statistically meaningful performance improvement over that of SVM, KLR and perceptron.
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