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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
BMCBI
2004
140views more  BMCBI 2004»
14 years 9 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
JUCS
2006
225views more  JUCS 2006»
14 years 9 months ago
Restricting the View and Connecting the Dots - Dangers of a Web Search Engine Monopoly
: Everyone realizes how powerful the few big Web search engine companies have become, both in terms of financial resources due to soaring stock quotes and in terms of the still hid...
Narayanan Kulathuramaiyer, Wolf-Tilo Balke
ICCV
2007
IEEE
15 years 11 months ago
A Study of Face Recognition as People Age
In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discard...
Haibin Ling, Stefano Soatto, Narayanan Ramanathan,...
ICPR
2004
IEEE
15 years 11 months ago
An Efficient Three-Stage Classifier for Handwritten Digit Recognition
This paper proposes an efficient three-stage classifier for handwritten digit recognition based on NN (Neural Network) and SVM (Support Vector Machine) classifiers. The classifica...
Dejan Gorgevik, Dusan Cakmakov