1 research outputs found
Learning and Forgetting with Local Information of New Objects
The performance of supervised learners depends on the presence of a
relatively large labeled sample. This paper proposes an automatic ongoing
learning system, which is able to incorporate new knowledge from the
experience obtained when classifying new objects and correspondingly, to
improve the efficiency of the system. We employ a stochastic rule for
classifying and editing, along with a condensing algorithm based on local
density to forget superfluous data (and control the sample size). The
effectiveness of the algorithm is experimentally evaluated using a number of
data sets taken from the UCI Machine Learning Database Repository