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» CC Prediction with Graphical Models
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ICML
2004
IEEE
14 years 8 months ago
Kernel conditional random fields: representation and clique selection
Kernel conditional random fields (KCRFs) are introduced as a framework for discriminative modeling of graph-structured data. A representer theorem for conditional graphical models...
John D. Lafferty, Xiaojin Zhu, Yan Liu
CGF
2007
135views more  CGF 2007»
13 years 7 months ago
Prediction of Individual Non-Linear Aging Trajectories of Faces
Represented in a Morphable Model, 3D faces follow curved trajectories in face space as they age. We present a novel algorithm that computes the individual aging trajectories for g...
Kristina Scherbaum, Martin Sunkel, Hans-Peter Seid...
KDD
2007
ACM
167views Data Mining» more  KDD 2007»
14 years 8 months ago
Multiscale topic tomography
Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic gr...
Ramesh Nallapati, Susan Ditmore, John D. Lafferty,...
DCC
2003
IEEE
14 years 7 months ago
PPM Model Cleaning
The Prediction by Partial Matching (PPM) algorithm uses a cumulative frequency count of input symbols in different contexts to estimate their probability distribution. Excellent c...
Milenko Drinic, Darko Kirovski, Miodrag Potkonjak
CG
2010
Springer
13 years 7 months ago
A cellular model for spatial population dynamics
Interacting populations exhibit complex behavior in nature. Classic quadratic iteration models with two or three populations exhibit some of the features seen in nature, but fail ...
Chu Yue (Stella) Dong, James T. Long, Clifford A. ...