Abstract
In this study, we construct the optimal Bayesian classifier (OBC) when the training observations are serially dependent. To model the effect of dependency, we assume the training observations are generated from VAR(p), which is a multi-dimensional vector autoregressive process of order p.
| Original language | English |
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| Title of host publication | 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781538679333 |
| DOIs | |
| Publication status | Published - Nov 16 2018 |
| Event | 2018 New York Scientific Data Summit, NYSDS 2018 - Upton, United States Duration: Aug 6 2018 → Aug 8 2018 |
Publication series
| Name | 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings |
|---|
Conference
| Conference | 2018 New York Scientific Data Summit, NYSDS 2018 |
|---|---|
| Country | United States |
| City | Upton |
| Period | 8/6/18 → 8/8/18 |
Keywords
- Optimal Bayesian Classification
- Vector Autoregressive Processes
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Signal Processing
- Modelling and Simulation
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Cite this
Zollanvari, A., & Dougherty, E. R. (2018). Optimal Bayesian Classification When the Training Observations are Serially Dependent. In 2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings [8538943] (2018 New York Scientific Data Summit, NYSDS 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NYSDS.2018.8538943