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» Analyzing the Effectiveness of Multiple-Detect Test Sets
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TNN
2008
178views more  TNN 2008»
13 years 7 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen
ICAC
2009
IEEE
14 years 2 months ago
Ranking the importance of alerts for problem determination in large computer systems
The complexity of large computer systems has raised unprecedented challenges for system management. In practice, operators often collect large volume of monitoring data from system...
Guofei Jiang, Haifeng Chen, Kenji Yoshihira, Akhil...
DGO
2008
126views Education» more  DGO 2008»
13 years 8 months ago
Active learning for e-rulemaking: public comment categorization
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
Stephen Purpura, Claire Cardie, Jesse Simons
BMCBI
2005
132views more  BMCBI 2005»
13 years 7 months ago
Kalign - an accurate and fast multiple sequence alignment algorithm
Background: The alignment of multiple protein sequences is a fundamental step in the analysis of biological data. It has traditionally been applied to analyzing protein families f...
Timo Lassmann, Erik L. L. Sonnhammer
KDD
2004
ACM
181views Data Mining» more  KDD 2004»
14 years 7 months ago
Column-generation boosting methods for mixture of kernels
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single...
Jinbo Bi, Tong Zhang, Kristin P. Bennett