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» Efficient Algorithms for Conditional Independence Inference
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NIPS
2008
13 years 9 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
TPDS
2008
80views more  TPDS 2008»
13 years 7 months ago
Scalable and Efficient End-to-End Network Topology Inference
To construct an efficient overlay network, the information of underlay is important. We consider using end-to-end measurement tools such as traceroute to infer the underlay topolog...
Xing Jin, Wanqing Tu, S.-H. Gary Chan
ECCV
2002
Springer
14 years 9 months ago
Factorial Markov Random Fields
In this paper we propose an extension to the standard Markov Random Field (MRF) model in order to handle layers. Our extension, which we call a Factorial MRF (FMRF), is analogous t...
Junhwan Kim, Ramin Zabih
ICML
2009
IEEE
14 years 2 months ago
Independent factor topic models
Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text document modeling. In this pap...
Duangmanee Putthividhya, Hagai Thomas Attias, Srik...
JIPS
2007
92views more  JIPS 2007»
13 years 7 months ago
Optimization of Domain-Independent Classification Framework for Mood Classification
In this paper, we introduce a domain-independent classification framework based on both k-nearest neighbor and Naïve Bayesian classification algorithms. The architecture of our s...
Sung-Pil Choi, Yuchul Jung, Sung-Hyon Myaeng