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» Online and batch learning of pseudo-metrics
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JSSPP
2007
Springer
13 years 11 months ago
A Self-optimized Job Scheduler for Heterogeneous Server Clusters
Heterogeneous clusters and grid infrastructures are becoming increasingly popular. In these computing infrastructures, machines have different resources, including memory sizes, d...
Elad Yom-Tov, Yariv Aridor
VLDB
2001
ACM
190views Database» more  VLDB 2001»
13 years 9 months ago
LEO - DB2's LEarning Optimizer
Most modern DBMS optimizers rely upon a cost model to choose the best query execution plan (QEP) for any given query. Cost estimates are heavily dependent upon the optimizer’s e...
Michael Stillger, Guy M. Lohman, Volker Markl, Mok...
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...
ASC
2008
13 years 5 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...
ITNG
2010
IEEE
13 years 10 months ago
A Fast and Stable Incremental Clustering Algorithm
— Clustering is a pivotal building block in many data mining applications and in machine learning in general. Most clustering algorithms in the literature pertain to off-line (or...
Steven Young, Itamar Arel, Thomas P. Karnowski, De...