Unsupervised On-Line Learning of Decision Trees for Hierarchical Data Analysis
Abstract
An adaptive on-line algorithm is proposed to estimate hierarchical data structures for non-stationary data sources. The approach is based on the principle of minimum cross entropy to derive a decision tree for data clustering and it employs a metalearning idea (learning to learn) to adapt to changes in data characteristics. Its efficiency is demonstrated by grouping non-stationary artifical data and by hierarchical segmentation of LANDSAT images.
Cite
Text
Held and Buhmann. "Unsupervised On-Line Learning of Decision Trees for Hierarchical Data Analysis." Neural Information Processing Systems, 1997.Markdown
[Held and Buhmann. "Unsupervised On-Line Learning of Decision Trees for Hierarchical Data Analysis." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/held1997neurips-unsupervised/)BibTeX
@inproceedings{held1997neurips-unsupervised,
title = {{Unsupervised On-Line Learning of Decision Trees for Hierarchical Data Analysis}},
author = {Held, Marcus and Buhmann, Joachim M.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {514-520},
url = {https://mlanthology.org/neurips/1997/held1997neurips-unsupervised/}
}