Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/28883
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Type: | Conference paper |
Title: | An application of minimum description length clustering to partitioning learning curves |
Author: | Navarro, D. Lee, M. |
Citation: | Proceedings of the 2005 IEEE International Symposium on Information Theory [electronic resource] : Adelaide, South Australia, Australia 4-9 September 2005: pp.587-591 |
Publisher: | 2005 IEEE |
Publisher Place: | Australia |
Issue Date: | 2005 |
ISBN: | 0780391519 |
Conference Name: | IEEE International Symposium on Information Theory (2005 : Adelaide, S. Aust.) |
Editor: | Abhayapala, T. Hanlen, L. |
Abstract: | We apply a Minimum Description Length–based clustering technique to the problem of partitioning a set of learning curves. The goal is to partition experimental data collected from different sources into groups of sources that are statistically the same.We solve this problem by defining statistical models for the data generating processes, then partitioning them using the Normalized Maximum Likelihood criterion. Unlike many alternative model selection methods, this approach which is optimal (in a minimax coding sense) for data of any sample size. We present an application of the method to the cognitive modeling problem of partitioning of human learning curves for different categorization tasks. |
Description: | © Copyright 2005 IEEE |
DOI: | 10.1109/ISIT.2005.1523403 |
Published version: | http://dx.doi.org/10.1109/isit.2005.1523403 |
Appears in Collections: | Aurora harvest 6 Environment Institute publications Psychology publications |
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