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|Title:||Sequence recognition with spatio-temporal long-term memory organization||Authors:||Nguyen, Vu Anh
Goh, Wooi Boon
Starzyk, Janusz A.
|Keywords:||DRNTU::Engineering::Computer science and engineering||Issue Date:||2012||Source:||Nguyen, V. A., Starzyk, J. A., & Goh, W. B. (2012). Sequence recognition with spatio-temporal long-term memory organization. The 2012 International Joint Conference on Neural Networks (IJCNN).||Abstract:||In this work, we propose a connectionist memory structure for spatio-temporal sequence learning and recognition inspired by the Long-Term Memory structure of human cortex. Besides symbolic data, our framework is able to continuously process real-valued multi-dimensional data stream. This capability is made possible by addressing three critical problems in spatio-temporal learning, namely error tolerance, significance of sequence's elements and memory forgetting mechanism. We demonstrate the potential of the framework with a synthetic example and a real world example, namely the task of hand-sign language interpretation with the Australian Sign Language dataset.||URI:||https://hdl.handle.net/10356/98284
|DOI:||http://dx.doi.org/10.1109/IJCNN.2012.6252682||Rights:||© 2012 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||SCSE Conference Papers|
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