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IJCAI
2003

Inductive Learning in Less Than One Sequential Data Scan

11 years 1 months ago
Inductive Learning in Less Than One Sequential Data Scan
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.
Wei Fan, Haixun Wang, Philip S. Yu, Shaw-hwa Lo
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where IJCAI
Authors Wei Fan, Haixun Wang, Philip S. Yu, Shaw-hwa Lo
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