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» Using Random Forests for Handwritten Digit Recognition
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CVPR
2009
IEEE
1528views Computer Vision» more  CVPR 2009»
16 years 1 months ago
Structured Output-Associative Regression
Structured outputs such as multidimensional vectors or graphs are frequently encountered in real world pattern recognition applications such as computer vision, natural language pr...
Liefeng Bo and Cristian Sminchisescu
TKDE
2011
479views more  TKDE 2011»
14 years 4 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang
JMLR
2010
185views more  JMLR 2010»
14 years 4 months ago
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
We introduce a simple order-based greedy heuristic for learning discriminative structure within generative Bayesian network classifiers. We propose two methods for establishing an...
Franz Pernkopf, Jeff A. Bilmes
ICDAR
2009
IEEE
15 years 4 months ago
Text Lines and Snippets Extraction for 19th Century Handwriting Documents Layout Analysis
In this paper we propose a new approach to improve electronic editions of human science corpus, providing an efficient estimation of manuscripts pages structure. In any handwriti...
Vincent Malleron, Véronique Eglin, Hubert E...
84
Voted
NIPS
1997
14 years 11 months ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung