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» Training of Support Vector Machines with Mahalanobis Kernels
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JMLR
2006
132views more  JMLR 2006»
13 years 7 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
14 years 8 months ago
Effective multi-label active learning for text classification
Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical ...
Bishan Yang, Jian-Tao Sun, Tengjiao Wang, Zheng Ch...
KDD
2006
ACM
174views Data Mining» more  KDD 2006»
14 years 8 months ago
Onboard classifiers for science event detection on a remote sensing spacecraft
Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a space...
Ashley Davies, Benjamin Cichy, Dominic Mazzoni, Ng...
GECCO
2007
Springer
235views Optimization» more  GECCO 2007»
14 years 1 months ago
Expensive optimization, uncertain environment: an EA-based solution
Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function e...
Maumita Bhattacharya
CIKM
2008
Springer
13 years 9 months ago
Learning a two-stage SVM/CRF sequence classifier
Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "ca...
Guilherme Hoefel, Charles Elkan