JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Machine learning methods on quantized vectors
Abstract
Vector quantization is one of the important issues in digital images. There are many studies conducted on quantized vectors or images. On the other hand, machine-learning approaches are a popular issue today. In this study, the classification performances of machine learning approaches on reduced image vectors are examined. Firstly, Corel 1K data set were reduced to 64 colors with octree and histogram feature vectors extracted. Classification was carried out using various machine learning approaches on the relevant vectors. As a result of the classification, the success of the methods was examined.

Keywords : Vector quantization, machine learning, corel 1k, classification

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Article available in :
Volume 1, Issue 2, 2023
Page : 46-49
https://doi.org/10.51271/JCEEES-0010
Article Information
Received : 15 Eyl 2023
Accepted : 19 Eki 2023
Published : 31 Eki 2023
Corresponding Author :

Ufuk Tanyeri: Ankara University

[email protected]
Citation : Tanyeri U, Dindar T, Kökver Y, Koçak NF. Machine learning methods on quantized vectors. J Comp Electr Electron Eng Sci. 2023;1(2):46-49.

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