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» GraphLab: A New Framework for Parallel Machine Learning
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WWW
2009
ACM
14 years 8 months ago
Personalized recommendation on dynamic content using predictive bilinear models
In Web-based services of dynamic content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendat...
Wei Chu, Seung-Taek Park
MCS
2010
Springer
14 years 20 days ago
Online Non-stationary Boosting
Abstract. Oza’s Online Boosting algorithm provides a version of AdaBoost which can be trained in an online way for stationary problems. One perspective is that this enables the p...
Adam Pocock, Paraskevas Yiapanis, Jeremy Singer, M...
NSPW
2006
ACM
14 years 1 months ago
Dark application communities
In considering new security paradigms, it is often worthwhile to anticipate the direction and nature of future attack paradigms. We identify a class of attacks based on the idea o...
Michael E. Locasto, Angelos Stavrou, Angelos D. Ke...
JMLR
2010
145views more  JMLR 2010»
13 years 2 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever
JCB
2008
159views more  JCB 2008»
13 years 7 months ago
BayesMD: Flexible Biological Modeling for Motif Discovery
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Man-Hung Eric Tang, Anders Krogh, Ole Winther