Abstract
A scheme of cellular recognition for phytoplankton species via statistical spatial analysis is presented in this paper. Bayesian Ying-Yang harmony learning system on Gaussian mixture models accomplishes automatic parameter learning and model selection in parallel, and roughly decides on the most competitive aggregate for possible genus or subgenus. With dipole kernel density estimate, probabilistic spatial geometrical coverage like hyper sausages exactly cognizes and matches against all the inclusive species in the specified aggregate. The mechanism guarantees that species inner knowledge could be explored in a coarse to fine process. Simulation experiment achieved probability distribution information, and proved the approach effective, superior and feasible.
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© 2006 Springer-Verlag Berlin Heidelberg
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Ji, G., Nian, R., Yang, S., Zhou, L., Feng, C. (2006). Cellular Recognition for Species of Phytoplankton Via Statistical Spatial Analysis. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_88
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DOI: https://doi.org/10.1007/978-3-540-37258-5_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
Online ISBN: 978-3-540-37258-5
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