Learning Different Types of New Attributes by Combining the Neural Network and Iterative Attribute Construction
Abstract
Most of the current constructive induction algorithms degrade performance as the target concept becomes larger and more complex in terms of Boolean combinations. Most are only capable of constructing relatively smaller new attributes. Though it is impossible to build a learner to learn any arbitrarily large and complex concept, there are some large and complex concepts that could be represented in a simple relation such as prototypical concepts, e.g., m-of-n, majority, etc. In this paper, we propose a new approach that combines the neural net and iterative attribute construction to learn relatively short but complex Boolean combinations and prototypical structures. We also carried a series of systematic experiments to characterize our approach.
Cite
Text
Hu. "Learning Different Types of New Attributes by Combining the Neural Network and Iterative Attribute Construction." European Conference on Machine Learning, 1997. doi:10.1007/3-540-62858-4_77Markdown
[Hu. "Learning Different Types of New Attributes by Combining the Neural Network and Iterative Attribute Construction." European Conference on Machine Learning, 1997.](https://mlanthology.org/ecmlpkdd/1997/hu1997ecml-learning/) doi:10.1007/3-540-62858-4_77BibTeX
@inproceedings{hu1997ecml-learning,
title = {{Learning Different Types of New Attributes by Combining the Neural Network and Iterative Attribute Construction}},
author = {Hu, Yuh-Jyh},
booktitle = {European Conference on Machine Learning},
year = {1997},
pages = {124-137},
doi = {10.1007/3-540-62858-4_77},
url = {https://mlanthology.org/ecmlpkdd/1997/hu1997ecml-learning/}
}