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» Using Temporal Data for Making Recommendations
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NIPS
1998
15 years 3 months ago
Approximate Learning of Dynamic Models
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Xavier Boyen, Daphne Koller
131
Voted
CONEXT
2008
ACM
15 years 4 months ago
Internet traffic classification demystified: myths, caveats, and the best practices
Recent research on Internet traffic classification algorithms has yielded a flurry of proposed approaches for distinguishing types of traffic, but no systematic comparison of the ...
Hyunchul Kim, Kimberly C. Claffy, Marina Fomenkov,...
193
Voted
SADFE
2008
IEEE
15 years 9 months ago
Computer Forensics in Forensis
Different users apply computer forensic systems, models, and terminology in very different ways. They often make incompatible assumptions and reach different conclusions about ...
Sean Peisert, Matt Bishop, Keith Marzullo
110
Voted
ICDM
2007
IEEE
116views Data Mining» more  ICDM 2007»
15 years 8 months ago
A Computational Approach to Style in American Poetry
We develop a quantitative method to assess the style of American poems and to visualize a collection of poems in relation to one another. Qualitative poetry criticism helped guide...
David M. Kaplan, David M. Blei
113
Voted
SDM
2010
SIAM
200views Data Mining» more  SDM 2010»
15 years 4 months ago
Residual Bayesian Co-clustering for Matrix Approximation
In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
Hanhuai Shan, Arindam Banerjee