Improved Chain-Block Algorithm to RVM on Large Scale Problems

摘要：

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 complexity 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.

关键词： RVM I-CBA machine learning regression

作者: GangLi Shu-BaoXing Hui-feng Xue

作者单位: College of Automation, Northwestern Polytechnical University, Xian Shaanxi 710072, China School of College of Automation, Northwestern Polytechnical University, Xian Shaanxi 710072, China

会议类型: 国际会议

会议名称: 2009 International Conference on Management of e-Commerce and e-Government ICMeCG 2009(第三届电子商务与电子政务管理国际会议)

会议地点: 南昌

会议语种:英文

页码: 205-208

在线出版日期: 2009-09-01（万方平台首次上网日期，不代表论文的发表时间）