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JMLR
2006
132views more  JMLR 2006»
13 years 9 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
TKDE
2008
134views more  TKDE 2008»
13 years 9 months ago
Sensor-Based Abnormal Human-Activity Detection
With the availability of affordable sensors and sensor networks, sensor-based human-activity recognition has attracted much attention in artificial intelligence and ubiquitous comp...
Jie Yin, Qiang Yang, Jeffrey Junfeng Pan
JMLR
2002
137views more  JMLR 2002»
13 years 9 months ago
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Masashi Sugiyama, Klaus-Robert Müller
ARTMED
2004
118views more  ARTMED 2004»
13 years 9 months ago
Bayesian fluorescence in situ hybridisation signal classification
Previous research has indicated the significance of accurate classification of fluorescence in situ hybridisation (FISH) signals for the detection of genetic abnormalities. Based ...
Boaz Lerner
KDD
2009
ACM
156views Data Mining» more  KDD 2009»
14 years 10 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...