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JMLR
2006
132views more  JMLR 2006»
13 years 5 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
MIR
2004
ACM
171views Multimedia» more  MIR 2004»
13 years 10 months ago
Mean version space: a new active learning method for content-based image retrieval
In content-based image retrieval, relevance feedback has been introduced to narrow the gap between low-level image feature and high-level semantic concept. Furthermore, to speed u...
Jingrui He, Hanghang Tong, Mingjing Li, HongJiang ...
TASLP
2008
108views more  TASLP 2008»
13 years 5 months ago
A Regression Approach to Music Emotion Recognition
Content-based retrieval has emerged in the face of content explosion as a promising approach to information access. In this paper, we focus on the challenging issue of recognizing ...
Yi-Hsuan Yang, Yu-Ching Lin, Ya-Fan Su, Homer H. C...
ISMB
2008
13 years 7 months ago
Classification of arrayCGH data using fused SVM
Motivation: Array-based comparative genomic hybridization (arrayCGH) has recently become a popular tool to identify DNA copy number variations along the genome. These profiles are...
Franck Rapaport, Emmanuel Barillot, Jean-Philippe ...
BMCBI
2010
224views more  BMCBI 2010»
13 years 5 months ago
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Susmita Datta, Vasyl Pihur, Somnath Datta