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» Learning Generative Models with the Up-Propagation Algorithm
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WINE
2005
Springer
136views Economy» more  WINE 2005»
15 years 3 months ago
Click Fraud Resistant Methods for Learning Click-Through Rates
Abstract. In pay-per-click online advertising systems like Google, Overture, or MSN, advertisers are charged for their ads only when a user clicks on the ad. While these systems ha...
Nicole Immorlica, Kamal Jain, Mohammad Mahdian, Ku...
CVPR
2009
IEEE
16 years 4 months ago
A Multi-View Probabilistic Model for 3D Object Classes
We propose a novel probabilistic framework for learning visual models of 3D object categories by combining appearance information and geometric constraints. Objects are represen...
Fei-Fei Li 0002, Hao Su, Min Sun, Silvio Savarese
CVPR
2004
IEEE
15 years 11 months ago
Model-Based Motion Clustering Using Boosted Mixture Modeling
Model-based clustering of motion trajectories can be posed as the problem of learning an underlying mixture density function whose components correspond to motion classes with dif...
Vladimir Pavlovic
EDUTAINMENT
2006
Springer
15 years 1 months ago
Research of Dynamic Terrain in Complex Battlefield Environments
In this paper, we present a novel method for dynamic terrain in battlefield and an efficient plan to simulate crater in the battle. We explore a few methods for dynamic terrain sur...
Xingquan Cai, Fengxia Li, Haiyan Sun, Shouyi Zhan
ICML
2005
IEEE
15 years 10 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...