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» Evaluating learning algorithms and classifiers
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CONEXT
2008
ACM
14 years 6 days ago
Internet traffic classification demystified: myths, caveats, and the best practices
Recent research on Internet traffic classification algorithms has yielded a flurry of proposed approaches for distinguishing types of traffic, but no systematic comparison of the ...
Hyunchul Kim, Kimberly C. Claffy, Marina Fomenkov,...
CIDM
2009
IEEE
14 years 3 months ago
An architecture and algorithms for multi-run clustering
—This paper addresses two main challenges for clustering which require extensive human effort: selecting appropriate parameters for an arbitrary clustering algorithm and identify...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Vadee...
STOC
2005
ACM
129views Algorithms» more  STOC 2005»
14 years 10 months ago
Learning with attribute costs
We study an extension of the "standard" learning models to settings where observing the value of an attribute has an associated cost (which might be different for differ...
Haim Kaplan, Eyal Kushilevitz, Yishay Mansour
SECON
2008
IEEE
14 years 4 months ago
Practical Algorithms for Gathering Stored Correlated Data in a Network
—Many sensing systems remotely monitor/measure an environment at several sites, and then report these observations to a central site. We propose and investigate several practical...
Ramin Khalili, James F. Kurose
ICDM
2010
IEEE
115views Data Mining» more  ICDM 2010»
13 years 8 months ago
Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times
Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for class...
Jin Shieh, Eamonn J. Keogh