文献类型：期刊文献

英文题名：A dynamic data correction algorithm based on polynomial smooth support vector machine

作者：Pu, Dong-Mei[1]; Gao, Da-Qi[1]; Yuan, Yu-Bo[1,2]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, Zhejiang, 316022, China

年份：2016

卷号：2

起止页码：820

外文期刊名：Proceedings - International Conference on Machine Learning and Cybernetics

收录：EI(收录号：20172603847293)

语种：英文

外文关键词：Polynomials - Quality control - Data handling - Support vector machines - Vectors

摘要：Data quality plays an important role in modern intelligent information system and is crucial to any data analysis task. Many imperfection-handling techniques avoid overfitting or simply remove offending portions of the data. Data correction can help to retain and recover as much information as possible from the original data resources. In this paper, we proposed a novel technique based on polynomial smooth support vector machine. The quadratic polynomial and the first degree of polynomial as the support vector machine smooth functions are investigated. At the same time, the function was used as smooth function to calculate compensation values. In order to show the procedures of our algorithm, some necessary steps need to be considered. Firstly, the original data are normalized, so as to eliminate experimental effects of dimensional problems. Secondly, the three different kinds of smooth functions need to be analysed mathematically. The difference measure are calculated to make sure the results of correction through different data correction models. The results of given noised data sets can show that the proposed the data correction method based on polynomial smooth support vector machine is effectiveness. ? 2016 IEEE.