{"title":"大规模RVM问题的改进链块算法","authors":"Gang Li, Shu-Bao Xing, Hui-feng Xue","doi":"10.1109/ICMECG.2009.21","DOIUrl":null,"url":null,"abstract":"RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose I-CBA based on CBA, I-CBA set iteration initial center as the iteration solution last time,reduce the time complexitiy further more with keeping high accuracy and sparsity simultaneously. Regression experiments with synthetical large benchmark data set demonstrates I-CBA yields state-of-the-art performance.","PeriodicalId":252323,"journal":{"name":"2009 International Conference on Management of e-Commerce and e-Government","volume":"255 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improved Chain-Block Algorithm to RVM on Large Scale Problems\",\"authors\":\"Gang Li, Shu-Bao Xing, Hui-feng Xue\",\"doi\":\"10.1109/ICMECG.2009.21\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose I-CBA based on CBA, I-CBA set iteration initial center as the iteration solution last time,reduce the time complexitiy further more with keeping high accuracy and sparsity simultaneously. Regression experiments with synthetical large benchmark data set demonstrates I-CBA yields state-of-the-art performance.\",\"PeriodicalId\":252323,\"journal\":{\"name\":\"2009 International Conference on Management of e-Commerce and e-Government\",\"volume\":\"255 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Management of e-Commerce and e-Government\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMECG.2009.21\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Management of e-Commerce and e-Government","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMECG.2009.21","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Chain-Block Algorithm to RVM on Large Scale Problems
RVM enables sparse classification and regression functions to be obtained by linearly-weighting a small number of fixed basis functions from a large dictionary of potential candidates.TOA on RVM has O(M3) time and O(M2) space complexity, where M is the training set size. It is thus computationally infeasible on very large data sets. We propose I-CBA based on CBA, I-CBA set iteration initial center as the iteration solution last time,reduce the time complexitiy further more with keeping high accuracy and sparsity simultaneously. Regression experiments with synthetical large benchmark data set demonstrates I-CBA yields state-of-the-art performance.