Learning and Forgetting with Local Information of New Objects
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/159830
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Learning and Forgetting with Local Information of New ObjectsDate
2008Publisher
Springer VerlagISBN
978-3-540-85920-8ISSN
03029743Bibliographic citation
Vázquez F.D., Sánchez J.S., Pla F. (2008) Learning and Forgetting with Local Information of New Objects. In: Ruiz-Shulcloper J., Kropatsch W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg.Type
info:eu-repo/semantics/conferenceObjectVersion
info:eu-repo/semantics/acceptedVersionSubject
Abstract
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
e ... [+]
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. [-]
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info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess