We propose an algorithm of global multiple sequence alignment that is based on a measure of what we call information discrepancy. The algorithm follows a progressive alignment ite...
Min Zhang, Weiwu Fang, Junhua Zhang, Zhongxian Chi
In a world where massive amounts of data are recorded on a large scale we need data mining technologies to gain knowledge from the data in a reasonable time. The Top Down Induction...
Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually in...
Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to ...
Lemuel R. Waitman, Douglas H. Fisher, Paul H. King
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...