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» Learning SVMs from Sloppily Labeled Data
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ICPR
2002
IEEE
14 years 10 months ago
Incorporating Conditional Independence Assumption with Support Vector Machines to Enhance Handwritten Character Segmentation Per
Learning Bayesian Belief Networks (BBN) from corpora and incorporating the extracted inferring knowledge with a Support Vector Machines (SVM) classifier has been applied to charac...
Manolis Maragoudakis, Ergina Kavallieratou, Nikos ...
NIPS
2008
13 years 10 months ago
Partially Observed Maximum Entropy Discrimination Markov Networks
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Jun Zhu, Eric P. Xing, Bo Zhang
WWW
2009
ACM
14 years 10 months ago
Automated construction of web accessibility models from transaction click-streams
Screen readers, the dominant assistive technology used by visually impaired people to access the Web, function by speaking out the content of the screen serially. Using screen rea...
Jalal Mahmud, Yevgen Borodin, I. V. Ramakrishnan, ...
EMNLP
2010
13 years 7 months ago
Clustering-Based Stratified Seed Sampling for Semi-Supervised Relation Classification
Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clusteri...
Longhua Qian, Guodong Zhou
ACL
2012
11 years 11 months ago
Automatic Event Extraction with Structured Preference Modeling
This paper presents a novel sequence labeling model based on the latent-variable semiMarkov conditional random fields for jointly extracting argument roles of events from texts. ...
Wei Lu, Dan Roth