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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
ICMLA
2009
14 years 7 months ago
Regularizing the Local Similarity Discriminant Analysis Classifier
Abstract--We investigate parameter-based and distributionbased approaches to regularizing the generative, similarity-based classifier called local similarity discriminant analysis ...
Luca Cazzanti, Maya R. Gupta
AIED
2007
Springer
15 years 3 months ago
Mind and Body: Dialogue and Posture for Affect Detection in Learning Environments
We investigated the potential of automatic detection of a learner’s affective states from posture patterns and dialogue features obtained from an interaction with AutoTutor, an i...
Sidney K. D'Mello, Arthur C. Graesser
ECML
2005
Springer
15 years 3 months ago
Learning from Positive and Unlabeled Examples with Different Data Distributions
Abstract. We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive exam...
Xiaoli Li, Bing Liu
ADBIS
1999
Springer
104views Database» more  ADBIS 1999»
15 years 1 months ago
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classifica...
Alexey Tsymbal, Seppo Puuronen, Vagan Y. Terziyan