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» A supervised learning approach for imbalanced data sets
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KDD
2004
ACM
135views Data Mining» more  KDD 2004»
14 years 8 months ago
Discovering additive structure in black box functions
Many automated learning procedures lack interpretability, operating effectively as a black box: providing a prediction tool but no explanation of the underlying dynamics that driv...
Giles Hooker
EMNLP
2007
13 years 9 months ago
Finding Good Sequential Model Structures using Output Transformations
In Sequential Viterbi Models, such as HMMs, MEMMs, and Linear Chain CRFs, the type of patterns over output sequences that can be learned by the model depend directly on the modelâ...
Edward Loper
TREC
2004
13 years 9 months ago
Feature Generation, Feature Selection, Classifiers, and Conceptual Drift for Biomedical Document Triage
We approached the problem of classifying papers for the TREC 2004 Genomics Track triage task as a four step process: feature generation, feature selection, classifier training, an...
Aaron M. Cohen, Ravi Teja Bhupatiraju, William R. ...
ICCV
2007
IEEE
14 years 9 months ago
Gradient Feature Selection for Online Boosting
Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work [23]. The marriage of rectangular features and integral-imageenabled fast...
Ting Yu, Xiaoming Liu 0002
ESWA
2006
122views more  ESWA 2006»
13 years 7 months ago
Transmembrane segments prediction and understanding using support vector machine and decision tree
In recent years, there have been many studies focusing on improving the accuracy of prediction of transmembrane segments, and many significant results have been achieved. In spite...
Jieyue He, Hae-Jin Hu, Robert W. Harrison, Phang C...