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CORR
2011
Springer
177views Education» more  CORR 2011»
14 years 7 months ago
Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS
Markov Logic Networks (MLNs) have emerged as a powerful framework that combines statistical and logical reasoning; they have been applied to many data intensive problems including...
Feng Niu, Christopher Ré, AnHai Doan, Jude ...
CVPR
2005
IEEE
16 years 1 months ago
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support ef...
Dragomir Anguelov, Benjamin Taskar, Vassil Chatalb...
MICCAI
2008
Springer
16 years 1 months ago
Markov Dependence Tree-Based Segmentation of Deep Brain Structures
We propose a new framework for multi-object segmentation of deep brain structures, which have significant shape variations and relatively small sizes in medical brain images. In th...
Jue Wu, Albert C. S. Chung
ICML
2006
IEEE
16 years 21 days ago
Cost-sensitive learning with conditional Markov networks
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks sup...
Prithviraj Sen, Lise Getoor
ICML
2000
IEEE
16 years 21 days ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...