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JMLR
2006
132views more  JMLR 2006»
14 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
MMM
2005
Springer
202views Multimedia» more  MMM 2005»
15 years 3 months ago
Image Mining and Retrieval Using Hierarchical Support Vector Machines
For some time now, image retrieval approaches have been developed that use low-level features, such as colour histograms, edge distributions and texture measures. What has been la...
Ross Brown, Binh Pham
IJON
2010
148views more  IJON 2010»
14 years 7 months ago
Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machin
Several solutions have been proposed to exploit the availability of heterogeneous sources of biomolecular data for gene function prediction, but few attention has been dedicated t...
Matteo Re, Giorgio Valentini
ISCAS
2005
IEEE
142views Hardware» more  ISCAS 2005»
15 years 3 months ago
Hardware-based support vector machine classification in logarithmic number systems
—Support Vector Machines are emerging as a powerful machine-learning tool. Logarithmic Number Systems (LNS) utilize the property of logarithmic compression for numerical operatio...
Faisal M. Khan, Mark G. Arnold, William M. Potteng...
TKDD
2008
113views more  TKDD 2008»
14 years 9 months ago
Privacy-preserving classification of vertically partitioned data via random kernels
We propose a novel privacy-preserving support vector machine (SVM) classifier for a data matrix A whose input feature columns are divided into groups belonging to different entiti...
Olvi L. Mangasarian, Edward W. Wild, Glenn Fung