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» Graph model selection using maximum likelihood
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VLDB
2007
ACM
155views Database» more  VLDB 2007»
14 years 10 months ago
Towards Graph Containment Search and Indexing
Given a set of model graphs D and a query graph q, containment search aims to find all model graphs g D such that q contains g (q g). Due to the wide adoption of graph models, f...
Chen Chen, Xifeng Yan, Philip S. Yu, Jiawei Han, D...
ICIP
2010
IEEE
13 years 8 months ago
View synthesis based on Conditional Random Fields and graph cuts
We propose a novel method to synthesize intermediate views from two stereo images and disparity maps that is robust to errors in disparity map. The proposed method computes a plac...
Lam C. Tran, Christopher J. Pal, Truong Q. Nguyen
PAMI
2006
168views more  PAMI 2006»
13 years 10 months ago
Graphical Models and Point Pattern Matching
This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We pres...
Tibério S. Caetano, Terry Caelli, Dale Schu...
ESEM
2008
ACM
13 years 12 months ago
A hybrid faulty module prediction using association rule mining and logistic regression analysis
This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key m...
Yasutaka Kamei, Akito Monden, Shuuji Morisaki, Ken...
ICML
2003
IEEE
14 years 11 months ago
Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions
An approach to semi-supervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, wit...
Xiaojin Zhu, Zoubin Ghahramani, John D. Lafferty