Share and Export
Share and Export
Baras, John S. and Dey, Subhrakanti (1999) Adaptive classification based on compressed data using learning vector quantization. In: Proceedings of the 38th IEEE Conference on Decision and Control. IEEE, pp. 3677-3683. ISBN 0780352505
Preview
adaptive.pdf
Download (679kB) | Preview
Abstract
Classification problems using compressed data are becoming increasingly important in many applications
with large amounts of sensory data and large sets
of classes. These applications range from aided target recognition (ATR), to medical diagnosis, to speech
recognition, to fault detection and identification in
manufacturing systems. In this paper, we develop and
analyze a learning vector quantization (LVQ) based
algorithm for the combined compression and classification problem. We show convergence of the algorithm using techniques from stochastic approximation,
namely, the ODE method.
| Item Type: | Book Section |
|---|---|
| Additional Information: | J. S. Baras and S. Dey, "Adaptive classification based on compressed data using learning vector quantization," Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304), 1999, pp. 3677-3683 vol.4, doi: 10.1109/CDC.1999.827925. |
| Keywords: | Learning vector quantization; classification; stochastic approximation; compression; non-parametric ; |
| Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 14436 |
| Identification Number: | 10.1109/CDC.1999.827925 |
| Depositing User: | Subhrakanti Dey |
| Date Deposited: | 18 May 2021 17:08 |
| Publisher: | IEEE |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/14436 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
- Item control page
Downloads
Downloads per month over past year
