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Analysis of Robust Soft Learning Vector Quantization

Vries, J.J.G. de (2007) Analysis of Robust Soft Learning Vector Quantization. Master's Thesis / Essay, Computing Science.

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Abstract

One of the popular methods for multiclass classification is Learning Vector Quantization (LVQ). There have been developed several variants of LVQ lately, among which Robust Soft Learning Vector Quantization, or RSLVQ for short. An introductory study showed that RSLVQ performs better than other LVQ algorithms, even very close to the optimal linear classifier, within a controlled environment. In order to study its performance in detail, we performed a mathematical analysis of the algorithm, in the form of a system of coupled Ordinary Differential Equations (ODE's), which might also help development of an optimal LVQ algorithm. Following from our analysis, we compare the potential performance of RSLVQ in relation to other LVQ variants and present a guideline for settings of the control parameter, i.e. the softness parameter.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/8966

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