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» Margin-Based Active Learning for Structured Output Spaces
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FOIKS
2008
Springer
14 years 4 months ago
Cost-minimising strategies for data labelling : optimal stopping and active learning
Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
Christos Dimitrakakis, Christian Savu-Krohn
ISVC
2010
Springer
13 years 6 months ago
On Supervised Human Activity Analysis for Structured Environments
We consider the problem of developing an automated visual solution for detecting human activities within industrial environments. This has been performed using an overhead view. Th...
Banafshe Arbab-Zavar, Imed Bouchrika, John N. Cart...
JBCB
2010
138views more  JBCB 2010»
13 years 2 months ago
Hierarchical Classification of Gene Ontology Terms Using the Gostruct Method
Protein function prediction is an active area of research in bioinformatics. And yet, transfer of annotation on the basis of sequence or structural similarity remains widely used ...
Artem Sokolov, Asa Ben-Hur
JUCS
2008
163views more  JUCS 2008»
13 years 7 months ago
Authoring Social-aware Tasks on Active Spaces
: Social-aware computing is an emerging trend based on ubiquitous computing technologies and collaborative work. A successful design demands a better understanding of group tasks, ...
Roberto F. Arroyo, Miguel Gea, José Luis Ga...
TIP
2010
155views more  TIP 2010»
13 years 6 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He