(A) study on improvement of high-dimensional function-based additive model = 고차원 함수 기반 가법 모형의 개선에 관한 연구

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dc.contributor.advisorKim, Sung-Ho-
dc.contributor.advisor김성호-
dc.contributor.authorHong, Minseong-
dc.date.accessioned2019-09-03T02:44:48Z-
dc.date.available2019-09-03T02:44:48Z-
dc.date.issued2019-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843277&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/266406-
dc.description학위논문(석사) - 한국과학기술원 : 수리과학과, 2019.2,[iii, 15 p. :]-
dc.description.abstractIn 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.languageeng-
dc.publisher한국과학기술원-
dc.subjectSparse Partially Linear Additive Models(SPLAM)▼alasso▼abias▼aloss function-
dc.subject희소 부분적 선형 가법 모형▼aLasso▼a편향▼a손실함수-
dc.title(A) study on improvement of high-dimensional function-based additive model = 고차원 함수 기반 가법 모형의 개선에 관한 연구-
dc.typeThesis(Master)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :수리과학과,-
dc.contributor.alternativeauthor홍민성-
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