Inductive Learning in Less Than One Sequential Data Scan
Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence. Pages 595-599.
Abstract
Most recent research of scalable inductive learning on very large dataset, decision tree construction in particular, focuses on eliminating memory constraints and reducing the number of sequential data scans. However, state-of-the-art decision tree construction algorithms still require multiple scans over the data set and use sophisticated control mechanisms and data structures. We first discuss a general inductive learning framework that scans the dataset exactly once. Then, we propose an extension based on Hoeffding's inequality that scans the dataset less than once. Our frameworks are applicable to a wide range of inductive learners.
@inproceedings{ijcai2003p88,
title = {Inductive Learning in Less Than One Sequential Data Scan},
author = {Fan, Wei and Wang, Haixun and Yu, Philip S. and Lo, Shaw-hwa},
booktitle = {Proceedings of the Eighteenth International Joint Conference on
Artificial Intelligence, {IJCAI-03}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Georg Gottlob},
pages = {595--599},
year = {2003},
url = {https://ijcai.org/proceedings/2003/88},
}
TY - CPAPER TI - Inductive Learning in Less Than One Sequential Data Scan AU - Fan, Wei AU - Wang, Haixun AU - Yu, Philip S. AU - Lo, Shaw-hwa T2 - Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, IJCAI-03 PB - International Joint Conferences on Artificial Intelligence PY - 2003 SP - 595 EP - 599 UR - https://ijcai.org/proceedings/2003/88 ER -Copied
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