Mathematical Considerations on Soft Learning Vector Quantisation and Robust Soft Learning Vector Quantisation
- Soft Learning Vector Quantisation (SLVQ) andRobust Soft Learning Vector Quantisation (RSLVQ) are supervised data classification methods, that have been applied successfully to real world classification problems. The performance of SLVQ and RSLVQ, however, reduces, when they are applied tomore complicated classification problems. In this thesis, we have introducedmodi-fications to SLVQand RSLVQ, in order to havemore capable versions of them. A few possibilities to modify SLVQ and RSLVQ are considered, some of them are not successful enough and they have been included for the sake of completeness. The fruits of the thesis are plenty, including Tangent Soft Learning Vector Quantisation-Strong (TSLVQ-S), together with its more stable version Tangent Robust Soft Learning Vector Quantisation-Strong (TRSLVQ-S), Attraction Soft Learning Vector Quantisation (ASLVQ) and Grassmannian Soft Learning Vector Quantisation (GSLVQ).
Author: | Mehrdad Mohannazadeh Bakhtiari |
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URN: | urn:nbn:de:bsz:mit1-opus4-104015 |
Advisor: | Thomas Villmann, Marika Kaden |
Document Type: | Master's Thesis |
Language: | English |
Year of Completion: | 2018 |
Granting Institution: | Hochschule Mittweida |
Release Date: | 2019/04/16 |
GND Keyword: | Vektorquantisierung |
Institutes: | Angewandte Computer‐ und Biowissenschaften |
DDC classes: | 621.3822 digitale Signalverarbeitung, Vektorquantisierung |
Open Access: | Frei zugänglich |
Licence (German): | Urheberrechtlich geschützt |