{"title":"基于原型的规则——一种理解数据的新方法","authors":"Wlodzislaw Duch, K. Grudzinski","doi":"10.1109/IJCNN.2001.938446","DOIUrl":null,"url":null,"abstract":"Logical rules are not the only way to understand the structure of data. Prototype-based rules evaluate similarity to a small set of prototypes using optimized similarity measures. Such rules include crisp and fuzzy logic rules as special cases and are natural way of categorization from psychological point of view. An elimination procedure selecting good prototypes from a training set has been described. Illustrative applications on several datasets show that a few prototypes may indeed explain the data structure.","PeriodicalId":346955,"journal":{"name":"IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)","volume":"235 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Prototype based rules-a new way to understand the data\",\"authors\":\"Wlodzislaw Duch, K. Grudzinski\",\"doi\":\"10.1109/IJCNN.2001.938446\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Logical rules are not the only way to understand the structure of data. Prototype-based rules evaluate similarity to a small set of prototypes using optimized similarity measures. Such rules include crisp and fuzzy logic rules as special cases and are natural way of categorization from psychological point of view. An elimination procedure selecting good prototypes from a training set has been described. Illustrative applications on several datasets show that a few prototypes may indeed explain the data structure.\",\"PeriodicalId\":346955,\"journal\":{\"name\":\"IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)\",\"volume\":\"235 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IJCNN.2001.938446\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN.2001.938446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Prototype based rules-a new way to understand the data
Logical rules are not the only way to understand the structure of data. Prototype-based rules evaluate similarity to a small set of prototypes using optimized similarity measures. Such rules include crisp and fuzzy logic rules as special cases and are natural way of categorization from psychological point of view. An elimination procedure selecting good prototypes from a training set has been described. Illustrative applications on several datasets show that a few prototypes may indeed explain the data structure.