DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Kim, Sung-Ho | - |
dc.contributor.advisor | 김성호 | - |
dc.contributor.author | Hong, Minseong | - |
dc.date.accessioned | 2019-09-03T02:44:48Z | - |
dc.date.available | 2019-09-03T02:44:48Z | - |
dc.date.issued | 2019 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843277&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/266406 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 수리과학과, 2019.2,[iii, 15 p. :] | - |
dc.description.abstract | In this paper, we introduce a Sparse Partially Linear Additive Models(SPLAM) to analyze a high-dimensional data. This is a model useful for variable selection and for checking if linearity of an independent variable is appropriate for explaining the dependent variable in an additive Model. We also introduce a method of bias reduction in parameter estimation by the Lasso. We introduce a new method of applying this method to the SPLAM to reduce the bias of parameter estimation in the SPLAM. To compare these two models, we use 3 loss functions for each data set. It is shown through experiments that the new method results in a decrease in error rate over the SPLAM. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Sparse Partially Linear Additive Models(SPLAM)▼alasso▼abias▼aloss function | - |
dc.subject | 희소 부분적 선형 가법 모형▼aLasso▼a편향▼a손실함수 | - |
dc.title | (A) study on improvement of high-dimensional function-based additive model | - |
dc.title.alternative | 고차원 함수 기반 가법 모형의 개선에 관한 연구 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :수리과학과, | - |
dc.contributor.alternativeauthor | 홍민성 | - |
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