{"title":"基于类概率输出网络的专利属性预测","authors":"W. Park","doi":"10.1109/ITCS.2010.5581303","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":166169,"journal":{"name":"2010 2nd International Conference on Information Technology Convergence and Services","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Prediction of a Patent Property Using the Class Probability Output Network\",\"authors\":\"W. Park\",\"doi\":\"10.1109/ITCS.2010.5581303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":166169,\"journal\":{\"name\":\"2010 2nd International Conference on Information Technology Convergence and Services\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Conference on Information Technology Convergence and Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ITCS.2010.5581303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Information Technology Convergence and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCS.2010.5581303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.