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» Algorithmic Randomness of Closed Sets
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115
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CORR
2010
Springer
143views Education» more  CORR 2010»
15 years 3 months ago
Quasi-randomness of graph balanced cut properties
Quasi-random graphs can be informally described as graphs whose edge distribution closely resembles that of a truly random graph of the same edge density. Recently, Shapira and Yu...
Hao Huang, Choongbum Lee
225
Voted
DAGM
2006
Springer
15 years 7 months ago
Near Real-Time Motion Segmentation Using Graph Cuts
We present a new approach to integrated motion estimation and segmentation by combining methods from discrete and continuous optimization. The velocity of each of a set of regions ...
Thomas Schoenemann, Daniel Cremers
138
Voted
BMCBI
2010
117views more  BMCBI 2010»
15 years 3 months ago
New decoding algorithms for Hidden Markov Models using distance measures on labellings
Background: Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries. Results: We give a set of algorithms to com...
Daniel G. Brown 0001, Jakub Truszkowski
163
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RECOMB
2005
Springer
16 years 3 months ago
Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition
Abstract. Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e. segmenta...
Yan Liu, Jaime G. Carbonell, Peter Weigele, Vanath...
163
Voted
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 7 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing