Abstract. We propose an approach to build a classifier composing consistent (100% confident) rules. Recently, associative classifiers that utilize association rules have been widel...
We outline an incremental learning algorithm designed for nonstationary environments where the underlying data distribution changes over time. With each dataset drawn from a new e...
Matthew T. Karnick, Michael Muhlbaier, Robi Polika...
Detecting impending failure of hard disks is an important prediction task which might help computer systems to prevent loss of data and performance degradation. Currently most of t...
Because of the large variation across different environments, a generic classifier trained on extensive data-sets may perform sub-optimally in a particular test environment. In th...
Acoustic events produced in meeting-room-like environments may carry information useful for perceptually aware interfaces. In this paper, we focus on the problem of combining diffe...