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KDD
2009
ACM
142views Data Mining» more  KDD 2009»
14 years 5 months ago
Quantification and semi-supervised classification methods for handling changes in class distribution
In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier. Further complicating this scenari...
Jack Chongjie Xue, Gary M. Weiss
SEMWEB
2007
Springer
13 years 11 months ago
DRIFT: A Framework for Ontology-based Design Support Systems
This paper proposes a framework for ontology-based design support systems, called DRIFT (Design Rationale Integration Framework of Three layers), which records, structures and retr...
Yutaka Nomaguchi, Kikuo Fujita
PAKDD
2004
ACM
137views Data Mining» more  PAKDD 2004»
13 years 10 months ago
Fast and Light Boosting for Adaptive Mining of Data Streams
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Fang Chu, Carlo Zaniolo
SAC
2005
ACM
13 years 10 months ago
Learning decision trees from dynamic data streams
: This paper presents a system for induction of forest of functional trees from data streams able to detect concept drift. The Ultra Fast Forest of Trees (UFFT) is an incremental a...
João Gama, Pedro Medas, Pedro Pereira Rodri...
JVCA
2007
128views more  JVCA 2007»
13 years 5 months ago
Impulse-based dynamic simulation in linear time
This paper describes an impulse-based dynamic simulation method for articulated bodies which has a linear time complexity. Existing linear-time methods are either based on a reduc...
Jan Bender