This study compares five well-known association rule algorithms using three real-world datasets and an artificial dataset. The experimental results confirm the performance improve...
Instruction fetch behavior has been shown to be very regular and predictable, even for diverse application areas. In this work, we propose the Lookahead Instruction Fetch Engine (...
Stephen Roderick Hines, Yuval Peress, Peter Gavin,...
We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead o...
We show how models for prediction with expert advice can be defined concisely and clearly using hidden Markov models (HMMs); standard HMM algorithms can then be used to efficientl...
We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic ...