Model fusion-based batch learning with application to oil spills detection

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TypeBook Chapter
Proceedings titleAdvanced Research in Applied Artificial Intelligence
Series titleLecture Notes In Computer Science; Volume 7345
Conference25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2012), June 9-12, 2012, Dalian, China
Pages4047; # of pages: 8
SubjectBatch Data; Batch Learning; Transfer Learning; Content-based Learning; Model Fusion; Oil Spill Detection
AbstractData split into batches is very common in real-world applications. In speech recognition and handwriting identification, the batches are different people. In areas like oil spill detection and train wheel failure prediction, the batches are the particular circumstances when the readings were recorded. The recent research has proved that it is important to respect the batch structure when learning models for batched data. We believe that such a batch structure is also an opportunity that can be exploited in the learning process. In this paper, we investigated the novel method for dealing with the batched data. We applied the developed batch learning techniques to detect oil spills using radar images collected from satellite stations. This paper reports some progress on the proposed batch learning method and the preliminary results obtained from oil spills detection.
Publication date
PublisherSpringer Berlin Heidelberg
AffiliationNational Research Council Canada; NRC Institute for Information Technology
Peer reviewedYes
NPARC number21261868
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Record identifier88a95ade-a09f-4fd7-9ed3-0d651f2bfb75
Record created2013-03-11
Record modified2016-07-11
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