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» A random graph model for massive graphs
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CVPR
1998
IEEE
16 years 6 months ago
Markov Random Fields with Efficient Approximations
Markov Random Fields (MRF's) can be used for a wide variety of vision problems. In this paper we focus on MRF's with two-valued clique potentials, which form a generaliz...
Yuri Boykov, Olga Veksler, Ramin Zabih
143
Voted
CIKM
2009
Springer
15 years 11 months ago
Group CRM: a new telecom CRM framework from social network perspective
The structure of customer communication network provides us a natural way to understand customers’ relationships. Traditional customer relationship management (CRM) methods focu...
Bin Wu, Qi Ye, Shengqi Yang, Bai Wang
NIPS
2003
15 years 6 months ago
Discriminative Fields for Modeling Spatial Dependencies in Natural Images
In this paper we present Discriminative Random Fields (DRF), a discriminative framework for the classification of natural image regions by incorporating neighborhood spatial depe...
Sanjiv Kumar, Martial Hebert
114
Voted
WWW
2009
ACM
16 years 5 months ago
Graffiti: node labeling in heterogeneous networks
We introduce a multi-label classification model and algorithm for labeling heterogeneous networks, where nodes belong to different types and different types have different sets of...
Ralitsa Angelova, Gjergji Kasneci, Fabian M. Sucha...
160
Voted
IJAHUC
2008
118views more  IJAHUC 2008»
15 years 4 months ago
Analysis models for unguided search in unstructured P2P networks
: Random walk and flooding are basic mechanisms for searching unstructured overlays. This paper shows that node coverage is an important metric for query performance in random grap...
Bin Wu, Ajay D. Kshemkalyani