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» MuFeSaC: Learning When to Use Which Feature Detector
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CVPR
2000
IEEE
14 years 11 months ago
Multimodal Speaker Detection Using Error Feedback Dynamic Bayesian Networks
Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous mu...
Vladimir Pavlovic, James M. Rehg, Ashutosh Garg, T...
CIBCB
2006
IEEE
14 years 3 months ago
Machine Learning in Basecalling Decoding Trace Peak Behaviour
— DNA sequence basecalling is commonly regarded as a solved problem, despite significant error rates being reflected in inaccuracies in databases and genome annotations. These er...
David Thornley, Stavros Petridis
AUSDM
2008
Springer
367views Data Mining» more  AUSDM 2008»
13 years 11 months ago
Categorical Proportional Difference: A Feature Selection Method for Text Categorization
Supervised text categorization is a machine learning task where a predefined category label is automatically assigned to a previously unlabelled document based upon characteristic...
Mondelle Simeon, Robert J. Hilderman
EMNLP
2006
13 years 10 months ago
Better Informed Training of Latent Syntactic Features
We study unsupervised methods for learning refinements of the nonterminals in a treebank. Following Matsuzaki et al. (2005) and Prescher (2005), we may for example split NP withou...
Markus Dreyer, Jason Eisner
CVPR
2012
IEEE
11 years 11 months ago
Pose pooling kernels for sub-category recognition
The ability to normalize pose based on super-category landmarks can significantly improve models of individual categories when training data are limited. Previous methods have co...
Ning Zhang, Ryan Farrell, Trevor Darrell