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NIPS
2004
14 years 11 months ago
An Application of Boosting to Graph Classification
This paper presents an application of Boosting for classifying labeled graphs, general structures for modeling a number of real-world data, such as chemical compounds, natural lan...
Taku Kudo, Eisaku Maeda, Yuji Matsumoto
NIPS
2004
14 years 11 months ago
Semi-Markov Conditional Random Fields for Information Extraction
We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semiCRF on an input sequence x outputs a "...
Sunita Sarawagi, William W. Cohen
ICMCS
2010
IEEE
193views Multimedia» more  ICMCS 2010»
14 years 10 months ago
Motion segmentation in compressed video using Markov Random Fields
In this paper, we propose an unsupervised segmentation algorithm for extracting moving objects/regions from compressed video using Markov Random Field (MRF) classification. First,...
Yue-Meng Chen, Ivan V. Bajic, Parvaneh Saeedi
CVPR
2010
IEEE
14 years 10 months ago
Anomaly detection in crowded scenes
A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable f...
Vijay Mahadevan, Weixin Li, Viral Bhalodia, Nuno V...
ENTCS
2008
102views more  ENTCS 2008»
14 years 9 months ago
Towards a Systematic Method for Proving Termination of Graph Transformation Systems
We describe a method for proving the termination of graph transformation systems. The method is based on the fact that infinite reductions must include infinite `creation chains&#...
Harrie Jan Sander Bruggink