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» A Learning System for Decision Support in Telecommunications
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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
CDC
2010
IEEE
106views Control Systems» more  CDC 2010»
13 years 3 months ago
Optimal cross-layer wireless control policies using TD learning
We present an on-line crosslayer control technique to characterize and approximate optimal policies for wireless networks. Our approach combines network utility maximization and ad...
Sean P. Meyn, Wei Chen, Daniel O'Neill
ACTAC
2006
126views more  ACTAC 2006»
13 years 8 months ago
Named Entity Recognition for Hungarian Using Various Machine Learning Algorithms
In this paper we introduce a statistical Named Entity recognizer (NER) system for the Hungarian language. We examined three methods for identifying and disambiguating proper nouns...
Richárd Farkas, György Szarvas, Andr&a...
IWCLS
2007
Springer
14 years 2 months ago
Evolving Fuzzy Rules with UCS: Preliminary Results
This paper presents Fuzzy-UCS, a Michigan-style Learning Fuzzy-Classifier System designed for supervised learning tasks. FuzzyUCS combines the generalization capabilities of UCS w...
Albert Orriols-Puig, Jorge Casillas, Ester Bernad&...
CIBCB
2009
IEEE
13 years 9 months ago
Application of machine learning approaches on quantitative structure activity relationships
Machine Learning techniques are successfully applied to establish quantitative relations between chemical structure and biological activity (QSAR), i.e. classify compounds as activ...
Mariusz Butkiewicz, Ralf Mueller, Danilo Selic, Er...