Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations
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Date
2016
Authors
Zollanvari, Amin
Dougherty, Edward R.
Journal Title
Journal ISSN
Volume Title
Publisher
Eurasip Journal on Bioinformatics and Systems Biology
Abstract
In classification, prior knowledge is incorporated in a Bayesian framework by assuming that the feature-label distribution belongs to an uncertainty class of feature-label distributions governed by a prior distribution. A posterior
distribution is then derived from the prior and the sample data. An optimal Bayesian classifier (OBC) minimizes the expected misclassification error relative to the posterior distribution. From an application perspective, prior
construction is critical.
Description
Keywords
Signal Processing, Applied Mathematics, Research Subject Categories::TECHNOLOGY::Engineering physics
Citation
Zollanvari, A., & Dougherty, E. R. (2016). Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations. Eurasip Journal on Bioinformatics and Systems Biology, 2016:2. DOI: 10.1186/s13637-016-0036-y