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» Learning from labeled and unlabeled data on a directed graph
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NIPS
2007
15 years 1 months ago
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
Maryam Mahdaviani, Tanzeem Choudhury
CVPR
2008
IEEE
16 years 1 months ago
Interactive image segmentation via minimization of quadratic energies on directed graphs
We propose a scheme to introduce directionality in the Random Walker algorithm for image segmentation. In particular, we extend the optimization framework of this algorithm to com...
Dheeraj Singaraju, Leo Grady, René Vidal
ICPR
2010
IEEE
15 years 5 months ago
Unsupervised Learning of Stroke Tagger for Online Kanji Handwriting Recognition
—Traditionally, HMM-based approaches to online Kanji handwriting recognition have relied on a hand-made dictionary, mapping characters to primitives such as strokes or substrokes...
Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara
EMNLP
2011
13 years 11 months ago
Class Label Enhancement via Related Instances
Class-instance label propagation algorithms have been successfully used to fuse information from multiple sources in order to enrich a set of unlabeled instances with class labels...
Zornitsa Kozareva, Konstantin Voevodski, Shang-Hua...
ICPR
2002
IEEE
16 years 26 days ago
Relational Graph Labelling Using Learning Techniques and Markov Random Fields
This paper introduces an approach for handling complex labelling problems driven by local constraints. The purpose is illustrated by two applications: detection of the road networ...
Denis Rivière, Jean-Francois Mangin, Jean-M...