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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 Name11th International Conference on Machine Learning and Computing, ICMLC 2019
Conference DateFebruary 22-24, 2019
Conference PlaceZhuhai, China
Author of SourceAsia Society of Researchers ; Metropolitan State University of Denver ; Southwest Jiaotong University ; University of Macau
Source Publication2019 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING
PublisherACM
Publication PlaceNew York
2019
Pages1-5
Indexed ByEI ; CPCI(ISTP)
EI Accession number20192307006454
WOS IDWOS:000477981500001
Contribution Rank1
ISBN978-1-4503-6600-7
KeywordStatistics machine learning regression model maximum likelihood
AbstractThe 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
Identifierhttp://ir.sia.cn/handle/173321/24686
Collection数字工厂研究室
Corresponding AuthorYang N(杨楠)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 1000049, China
Recommended Citation
GB/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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