Modification of the Classification-by-Component Predictor Using Dempster-Shafer-Theory

Bakhtiari, Mehrdad Mohannazadeh; Villmann, Thomas

doi:10.1007/978-3-031-15444-7_5

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2023

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“…The flexibility of components in LVQ may cause instability in learning. In case of Classificationby-components (CbC) [4], modified CbC [5] redefines the reasoning parameters for a more stable learning process. Nonetheless, both CbC and the modified version depend on initialization of the components.…”

Section: Introductionmentioning

confidence: 99%

Learning Vector Quantization in Context of Information Bottleneck Theory

Mohannazadeh Bakhtiari,

Staps,

Villmann

2023

ESANN 2023 Proceesdings

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This paper is an effort to parameterize Information Bottle-neck Theory to become a supervised classifier. We introduce a parametrization by means of Learning Vector Quantization. With this new approach, one can find suitable components that are necessary for an accurate, yet efficient, classification. A balance between compression and representation is made by means of a specially designed objective function.

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Section: Introductionmentioning

confidence: 99%