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CVPR
2007
IEEE
16 years 2 months ago
Combining Static Classifiers and Class Syntax Models for Logical Entity Recognition in Scanned Historical Documents
Class syntax can be used to 1) model temporal or locational evolvement of class labels of feature observation sequences, 2) correct classification errors of static classifiers if ...
Song Mao, Praveer Mansukhani, George R. Thoma
CVPR
2005
IEEE
16 years 2 months ago
A Two Level Approach for Scene Recognition
Classifying pictures into one of several semantic categories is a classical image understanding problem. In this paper, we present a stratified approach to both binary (outdoor-in...
Le Lu, Kentaro Toyama, Gregory D. Hager
JFR
2010
119views more  JFR 2010»
14 years 7 months ago
Unsupervised classification of dynamic obstacles in urban environments
This paper presents a solution to the problem of unsupervised classification of dynamic obstacles in urban environments. A track-based model is introduced for the integration of 2...
Roman Katz, Juan Nieto, Eduardo Mario Nebot
ML
2010
ACM
135views Machine Learning» more  ML 2010»
14 years 7 months ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
97
Voted
ICASSP
2009
IEEE
15 years 7 months ago
Active learning for semi-supervised multi-task learning
We present an algorithm for active learning (adaptive selection of training data) within the context of semi-supervised multi-task classifier design. The semi-supervised multi-ta...
Hui Li, Xuejun Liao, Lawrence Carin