Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations
| dc.contributor.author | Zollanvari, Amin | |
| dc.contributor.author | Dougherty, Edward R. | |
| dc.date.accessioned | 2017-01-06T09:01:44Z | |
| dc.date.available | 2017-01-06T09:01:44Z | |
| dc.date.issued | 2016 | |
| dc.description.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. | ru_RU |
| dc.identifier.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 | ru_RU |
| dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/2187 | |
| dc.language.iso | en | ru_RU |
| dc.publisher | Eurasip Journal on Bioinformatics and Systems Biology | ru_RU |
| dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
| dc.subject | Signal Processing | ru_RU |
| dc.subject | Applied Mathematics | ru_RU |
| dc.subject | Research Subject Categories::TECHNOLOGY::Engineering physics | ru_RU |
| dc.title | Incorporating prior knowledge induced from stochastic differential equations in the classification of stochastic observations | ru_RU |
| dc.type | Article | ru_RU |