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Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost
2010
2010 Ninth International Conference on Machine Learning and Applications
The application of learning-based vision techniques to real scenarios usually requires a tunning procedure, which involves the acquisition and labeling of new data and in situ experiments in order to adapt the learning algorithm to each scenario. We address an automatic update procedure of the L2boost algorithm that is able to adapt the initial models learned off-line. Our method is named UAL2Boost and present three new contributions: (i) an on-line and continuous procedure that updates
doi:10.1109/icmla.2010.143
dblp:conf/icmla/RibeiroMS10
fatcat:3qpurehbfvhxzja35j6fvnanu4