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105
Voted
NIPS
2004
15 years 2 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
15 years 2 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»
15 years 1 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
100
Voted
CVPR
2010
IEEE
15 years 29 days 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»
15 years 25 days 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