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ICASSP
2011
IEEE
13 years 7 days ago
Application specific loss minimization using gradient boosting
Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...
IUI
2009
ACM
14 years 5 months ago
Using salience to segment desktop activity into projects
Knowledge workers must manage large numbers of simultaneous, ongoing projects that collectively involve huge numbers of resources (documents, emails, web pages, calendar items, et...
Daniel Lowd, Nicholas Kushmerick
MLDM
2007
Springer
14 years 2 months ago
Transductive Learning from Relational Data
Transduction is an inference mechanism “from particular to particular”. Its application to classification tasks implies the use of both labeled (training) data and unlabeled (...
Michelangelo Ceci, Annalisa Appice, Nicola Barile,...
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
UM
2010
Springer
14 years 1 months ago
Modeling Long-Term Search Engine Usage
Search engines are key components in the online world and the choice of search engine is an important determinant of the user experience. In this work we seek to model user behavio...
Ryen W. White, Ashish Kapoor, Susan T. Dumais