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» Causal inference using the algorithmic Markov condition
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JCB
2006
215views more  JCB 2006»
14 years 9 months ago
Protein Fold Recognition Using Segmentation Conditional Random Fields (SCRFs)
Protein fold recognition is an important step towards understanding protein three-dimensional structures and their functions. A conditional graphical model, i.e., segmentation con...
Yan Liu 0002, Jaime G. Carbonell, Peter Weigele, V...
IWANN
2005
Springer
15 years 3 months ago
Adaptive Power System Stabilizer Using ANFIS and Genetic Algorithms
— This paper presents an adaptive Power System Stabilizer (PSS) using an Adaptive Network Based Fuzzy Inference System (ANFIS) and Genetic Algorithms (GAs). Firstly, genetic algo...
Jesús Fraile-Ardanuy, Pedro J. Zufiria
ATAL
2007
Springer
15 years 4 months ago
Conditional random fields for activity recognition
Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we...
Douglas L. Vail, Manuela M. Veloso, John D. Laffer...
ICIP
2008
IEEE
15 years 11 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
CVPR
2006
IEEE
15 years 12 months ago
AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition
Activity recognition is an important issue in building intelligent monitoring systems. We address the recognition of multilevel activities in this paper via a conditional Markov r...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh, ...