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ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
14 years 3 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
CVIU
2006
162views more  CVIU 2006»
13 years 8 months ago
Unsupervised scene analysis: A hidden Markov model approach
This paper presents a new approach to scene analysis, which aims at extracting structured information from a video sequence using directly low-level data. The method models the se...
Manuele Bicego, Marco Cristani, Vittorio Murino
BMCBI
2008
130views more  BMCBI 2008»
13 years 8 months ago
An enhanced partial order curve comparison algorithm and its application to analyzing protein folding trajectories
Background: Understanding how proteins fold is essential to our quest in discovering how life works at the molecular level. Current computation power enables researchers to produc...
Hong Sun, Hakan Ferhatosmanoglu, Motonori Ota, Yus...
CVPR
2003
IEEE
14 years 10 months ago
Clustering Appearances of Objects Under Varying Illumination Conditions
We introduce two appearance-based methods for clustering a set of images of 3-D objects, acquired under varying illumination conditions, into disjoint subsets corresponding to ind...
Jeffrey Ho, Ming-Hsuan Yang, Jongwoo Lim, Kuang-Ch...
MICCAI
2005
Springer
14 years 9 months ago
Support Vector Clustering for Brain Activation Detection
In this paper, we propose a new approach to detect activated time series in functional MRI using support vector clustering (SVC). We extract Fourier coefficients as the features of...
Defeng Wang, Lin Shi, Daniel S. Yeung, Pheng-Ann H...