{"title":"采用多元多项式的模式分类","authors":"K. Toh","doi":"10.1109/ISSNIP.2014.6827591","DOIUrl":null,"url":null,"abstract":"The use of a full multivariate polynomial model for predictor learning was deemed a daunting task due to its explosive number of expansion terms for high dimensional inputs and high order models. This paper investigates into the viability of using full multivariate polynomials for predictor learning. Particularly, we investigate into the frequently encountered under-determined system with an estimation formulation based on a ridge regression beyond the commonly known primal and dual forms. Extensive experiments are performed to observe the predictor learning properties on polynomial models beyond the frequently adopted second order.","PeriodicalId":269784,"journal":{"name":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Pattern classification adopting multivariate polynomials\",\"authors\":\"K. Toh\",\"doi\":\"10.1109/ISSNIP.2014.6827591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The use of a full multivariate polynomial model for predictor learning was deemed a daunting task due to its explosive number of expansion terms for high dimensional inputs and high order models. This paper investigates into the viability of using full multivariate polynomials for predictor learning. Particularly, we investigate into the frequently encountered under-determined system with an estimation formulation based on a ridge regression beyond the commonly known primal and dual forms. Extensive experiments are performed to observe the predictor learning properties on polynomial models beyond the frequently adopted second order.\",\"PeriodicalId\":269784,\"journal\":{\"name\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISSNIP.2014.6827591\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSNIP.2014.6827591","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The use of a full multivariate polynomial model for predictor learning was deemed a daunting task due to its explosive number of expansion terms for high dimensional inputs and high order models. This paper investigates into the viability of using full multivariate polynomials for predictor learning. Particularly, we investigate into the frequently encountered under-determined system with an estimation formulation based on a ridge regression beyond the commonly known primal and dual forms. Extensive experiments are performed to observe the predictor learning properties on polynomial models beyond the frequently adopted second order.