Recurrent Learning Vector Quantization
- Learning Vector Quantization (LVQ) methods have been popular choices of classification models ever since its introduction by T. Kohonen in the 90s. These days, LVQ is combined with Deep Learning methods to provide powerful yet interpretable machine-learning solutions to some of the most challenging computational problems. However, techniques to model recurrent relationships in the data using prototype methods still remain quite unsophisticated. In particular, we are not aware of any modification of LVQ that allows the input data to have different lengths. Needless to say, such data is abundant in today's digital world and demands new processing techniques to extract useful information. In this paper, we propose the use of the Siamese architecture to not only model recurrent relationships within the prototypes but also the ability to handle prototypes of various dimensions simultaneously.
Author: | Jensun Ravichandran, Thomas Villmann |
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DOI: | https://doi.org/10.48446/opus-12297 |
ISSN: | 1437-7624 |
Parent Title (German): | 26. Interdisziplinäre Wissenschaftliche Konferenz Mittweida |
Publisher: | Hochschule Mittweida |
Place of publication: | Mittweida |
Document Type: | Conference Proceeding |
Language: | English |
Year of Completion: | 2021 |
Publishing Institution: | Hochschule Mittweida |
Contributing Corporation: | Saxon Institute for Computational Intelligence and Machine Learning |
Release Date: | 2021/05/18 |
Tag: | Interpretable Models; Prototye-based models; Recurent Neural Networks |
Issue: | 002 |
Page Number: | 2 |
First Page: | 147 |
Last Page: | 148 |
Open Access: | Frei zugänglich |
Licence (German): | Urheberrechtlich geschützt |