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 Model loss and distribution analysis of regression problems in machine learning Yang N(杨楠)1,2,3; Zheng ZY(郑泽宇)1,2,3; Wang TR(王天然)1,2,3 Department 数字工厂研究室 Conference Name 11th International Conference on Machine Learning and Computing, ICMLC 2019 Conference Date February 22-24, 2019 Conference Place Zhuhai, China Author of Source Asia Society of Researchers ; Metropolitan State University of Denver ; Southwest Jiaotong University ; University of Macau Source Publication 2019 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING Publisher ACM Publication Place New York 2019 Pages 1-5 Indexed By EI ; CPCI（ISTP） EI Accession number 20192307006454 WOS ID WOS:000477981500001 Contribution Rank 1 ISBN 978-1-4503-6600-7 Keyword Statistics machine learning regression model maximum likelihood Abstract The machine learning regression model is based on the assumption of normal distribution. In this paper, we mainly study the probability distribution of the machine learning model and the effect of the convergence values of different loss functions on the probability distribution model. Based on the idea of robust regression and the assumption of homogeneous variance of the model, we solved the statistical solution of two-dimensional regression problem by using least square method. The maximum likelihood estimation parameters of the probabilistic model are obtained by using the maximum likelihood estimation method. In order to compare the solving parameters of the two methods, the convergence values of L1 loss function and L2 loss function are used for the regression verification. Through the mathematical and statistical rigorous derivation, obtained two important conclusions; First, under the condition that the data satisfies normal distribution and is based on the assumption of homogeneous variance, the probability model conforms to the multivariate gaussian distribution. Secondly, the model satisfying the multi-gaussian distribution has little influence on the parameter estimation under the condition of the large number theorem, that is, the multi-gaussian distribution model has good tolerance to the loss function. © 2019 Association for Computing Machinery. Language 英语 Citation statistics Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS] Document Type 会议论文 Identifier http://ir.sia.cn/handle/173321/24686 Collection 数字工厂研究室 Corresponding Author Yang N(杨楠) Affiliation 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China3.University of Chinese Academy of Sciences, Beijing 1000049, China Recommended CitationGB/T 7714 Yang N,Zheng ZY,Wang TR. Model loss and distribution analysis of regression problems in machine learning[C]//Asia Society of Researchers, Metropolitan State University of Denver, Southwest Jiaotong University, University of Macau. New York:ACM,2019:1-5.
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