Neural Information Processing Systems / Amazon / Monte Carlo / Morgan Kaufmann Publishers Inc. / /
Country
Jordan / United States / Australia / /
Currency
USD / / /
Facility
University of Washington-Seattle / Engineering University of California / /
IndustryTerm
real-world applications / online store / food facilities / online advertising / food production plants / mining / recommender systems / social networks / social network / large recommender systems / /
Organization
Royal Statistical Society / Center for Statistics and the Social Sciences / Charles Elkan Department of Computer Science / Computer Science and Engineering University / University of Washington-Seattle / Dyadic Prediction Aditya Krishna Menon Department of Computer Science / University of California / San Diego / /
Person
Given / Charles Elkan / D. M. Rennie / N. Srebro / Krishna Menon / / /
Position
author / Governor / LFL model for both nominal and ordinal labels / log-linear model for dyadic prediction / rT / representative / simple log-linear model / Statistician / vol / /
Product
Pentax K-x Digital Camera / /
ProgrammingLanguage
MATLAB / /
PublishedMedium
Journal of the Royal Statistical Society / Machine Learning / /
Technology
RAM / training algorithm / Data Mining / Machine Learning / gesture recognition / /
2010 IEEE International Conference on Data Mining A Log-Linear Model with Latent Features for Dyadic Prediction Aditya Krishna Menon Department of Computer Science and Engineering University of California, San Diego