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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
AUSDM
2008
Springer
367views Data Mining» more  AUSDM 2008»
13 years 10 months ago
Categorical Proportional Difference: A Feature Selection Method for Text Categorization
Supervised text categorization is a machine learning task where a predefined category label is automatically assigned to a previously unlabelled document based upon characteristic...
Mondelle Simeon, Robert J. Hilderman
KDD
2004
ACM
196views Data Mining» more  KDD 2004»
14 years 9 months ago
Adversarial classification
Essentially all data mining algorithms assume that the datagenerating process is independent of the data miner's activities. However, in many domains, including spam detectio...
Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. ...
ICMCS
2006
IEEE
177views Multimedia» more  ICMCS 2006»
14 years 2 months ago
Mixed Type Audio Classification with Support Vector Machine
Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story se...
Lei Chen 0002, Sule Gündüz, M. Tamer &Ou...
SDM
2010
SIAM
218views Data Mining» more  SDM 2010»
13 years 10 months ago
Confidence-Based Feature Acquisition to Minimize Training and Test Costs
We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test...
Marie desJardins, James MacGlashan, Kiri L. Wagsta...