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Instantaneous learning for Learning Vector Quantization variant based on reject options

  • Digital data is rising day by day and so is the need for intelligent, automated data processing in daily life. In addition to this, in machine learning, a secure and accurate way to classify data is important. This holds utmost importance in certain fields, e.g. in medical data analysis. Moreover, in order to avoid severe consequences, the accuracy and reliability of the classification are equally important. So if the classification is not reliable, instead of accepting the wrongly classified data point, it is better to reject such a data point. This can be done with the help of some strategies by using them on top of a trained model or including them directly in the objective function of the desired training model. We discuss such strategies and analyze the results on data sets in this thesis.

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Author:Hassan Amjad
Advisor:Thomas Villmann, Marika Kaden
Document Type:Master's Thesis
Year of Completion:2022
Granting Institution:Hochschule Mittweida
Release Date:2022/11/21
GND Keyword:Maschinelles Lernen
Page Number:65
Institutes:Angewandte Computer‐ und Bio­wissen­schaften
DDC classes:006.31 Maschinelles Lernen
Open Access:Frei zugänglich
Licence (German):License LogoUrheberrechtlich geschützt