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» Support Vector Machine for Multiple Feature Classifcation
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JMLR
2006
132views more  JMLR 2006»
13 years 8 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
BMCBI
2004
140views more  BMCBI 2004»
13 years 7 months ago
What can we learn from noncoding regions of similarity between genomes?
Background: In addition to known protein-coding genes, large amounts of apparently non-coding sequence are conserved between the human and mouse genomes. It seems reasonable to as...
Thomas A. Down, Tim J. P. Hubbard
BMCBI
2010
97views more  BMCBI 2010»
13 years 8 months ago
Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear a
Background: Protein kinases play crucial roles in cell growth, differentiation, and apoptosis. Abnormal function of protein kinases can lead to many serious diseases, such as canc...
Maris Lapinsh, Jarl E. S. Wikberg
GECCO
2008
Springer
141views Optimization» more  GECCO 2008»
13 years 9 months ago
Managing team-based problem solving with symbiotic bid-based genetic programming
Bid-based Genetic Programming (GP) provides an elegant mechanism for facilitating cooperative problem decomposition without an a priori specification of the number of team member...
Peter Lichodzijewski, Malcolm I. Heywood
CIVR
2009
Springer
132views Image Analysis» more  CIVR 2009»
14 years 2 months ago
Real-time bag of words, approximately
We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipelin...
Jasper R. R. Uijlings, Arnold W. M. Smeulders, Rem...