Modeling Life Time Data by Neural Networks

Young B. Moon, Hyune-Ju Kim
Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, PMLR R0:396-402, 1995.

Abstract

With the advancement of sophisticated computer programs, much of the data analysis process such as graph drawing, hypothesis testing, and construction of interval estimates can be automated. One exception is the process of interpreting graphical data, which is still being done by trained statisticians. The efforts of computerizing the interpretation process of graphical data must address at least two issues. First, we need to incorporate the flexibility of trained statisticians. Second, we need to incorporate desirable subjectivity of experienced statisticians. This paper presents a method which automates the process of graphical analysis using neural networks trained by the Back-propagation learning rule. Two case studies were performed to demonstrate the feasibility of the method. Particularly, the empirical case study has demonstrated the effectiveness of the neural network approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR0-moon95a, title = {Modeling Life Time Data by Neural Networks}, author = {Moon, Young B. and Kim, Hyune-Ju}, booktitle = {Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics}, pages = {396--402}, year = {1995}, editor = {Fisher, Doug and Lenz, Hans-Joachim}, volume = {R0}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/r0/moon95a/moon95a.pdf}, url = {https://proceedings.mlr.press/r0/moon95a.html}, abstract = {With the advancement of sophisticated computer programs, much of the data analysis process such as graph drawing, hypothesis testing, and construction of interval estimates can be automated. One exception is the process of interpreting graphical data, which is still being done by trained statisticians. The efforts of computerizing the interpretation process of graphical data must address at least two issues. First, we need to incorporate the flexibility of trained statisticians. Second, we need to incorporate desirable subjectivity of experienced statisticians. This paper presents a method which automates the process of graphical analysis using neural networks trained by the Back-propagation learning rule. Two case studies were performed to demonstrate the feasibility of the method. Particularly, the empirical case study has demonstrated the effectiveness of the neural network approach.}, note = {Reissued by PMLR on 01 May 2022.} }
Endnote
%0 Conference Paper %T Modeling Life Time Data by Neural Networks %A Young B. Moon %A Hyune-Ju Kim %B Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1995 %E Doug Fisher %E Hans-Joachim Lenz %F pmlr-vR0-moon95a %I PMLR %P 396--402 %U https://proceedings.mlr.press/r0/moon95a.html %V R0 %X With the advancement of sophisticated computer programs, much of the data analysis process such as graph drawing, hypothesis testing, and construction of interval estimates can be automated. One exception is the process of interpreting graphical data, which is still being done by trained statisticians. The efforts of computerizing the interpretation process of graphical data must address at least two issues. First, we need to incorporate the flexibility of trained statisticians. Second, we need to incorporate desirable subjectivity of experienced statisticians. This paper presents a method which automates the process of graphical analysis using neural networks trained by the Back-propagation learning rule. Two case studies were performed to demonstrate the feasibility of the method. Particularly, the empirical case study has demonstrated the effectiveness of the neural network approach. %Z Reissued by PMLR on 01 May 2022.
APA
Moon, Y.B. & Kim, H.. (1995). Modeling Life Time Data by Neural Networks. Pre-proceedings of the Fifth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R0:396-402 Available from https://proceedings.mlr.press/r0/moon95a.html. Reissued by PMLR on 01 May 2022.

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