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DATAMINE
1999
108views more  DATAMINE 1999»
15 years 4 months ago
A Survey of Methods for Scaling Up Inductive Algorithms
Abstract. One of the de ning challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, ...
Foster J. Provost, Venkateswarlu Kolluri
ICDM
2007
IEEE
122views Data Mining» more  ICDM 2007»
15 years 10 months ago
Noise Modeling with Associative Corruption Rules
This paper presents an active learning approach to the problem of systematic noise inference and noise elimination, specifically the inference of Associated Corruption (AC) rules...
Yan Zhang, Xindong Wu
PAKDD
2005
ACM
132views Data Mining» more  PAKDD 2005»
15 years 10 months ago
SETRED: Self-training with Editing
Self-training is a semi-supervised learning algorithm in which a learner keeps on labeling unlabeled examples and retraining itself on an enlarged labeled training set. Since the s...
Ming Li, Zhi-Hua Zhou
DSD
2007
IEEE
120views Hardware» more  DSD 2007»
15 years 10 months ago
Latency Minimization for Synchronous Data Flow Graphs
Synchronous Data Flow Graphs (SDFGs) are a very useful means for modeling and analyzing streaming applications. Some performance indicators, such as throughput, have been studied b...
Amir Hossein Ghamarian, Sander Stuijk, Twan Basten...
CF
2009
ACM
15 years 9 months ago
Data parallel acceleration of decision support queries using Cell/BE and GPUs
Decision Support System (DSS) workloads are known to be one of the most time-consuming database workloads that processes large data sets. Traditionally, DSS queries have been acce...
Pedro Trancoso, Despo Othonos, Artemakis Artemiou