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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1. Lloyd, S. P. (1957). Least square quantization in PCM. Bell TelephoneLaboratories Paper. Published in journal much later: Lloyd, SP: Leastsquares quantization in PCM. IEEE Trans. Inform. Theor. (1957/1982),18(11).
2. Forgy, E. W. (1965). Cluster analysis of multivariate data: efficiencyversus interpretability of classifications. Biometrics, 21, 768-769.
3. Lejeune Dirichlet, G. (1850). Over the reduction of the positive squareshapes with three indefinite entire numbers . Journal for the pure andapplied Mathematics ( Crelles Journal ), 1850(40), 209-227.
4. Voronoi, G. (1908). News apps of the settings continue with thetheory of the shapes quadratic. Second memory. Research on _parallelohedrons primitives . Newspaper for die queen und angewandteMathematik (Crelles Journal), 1908(134), 198-287.
5. Linde, Y., Buzo, A., & Gray, R. (1980). An algorithm for vector quantizerdesign. IEEE Transactions on communications, 28(1), 84-95.
6. Gersho, A. (1982). On the structure of vector quantizers. IEEETransactions on Information Theory, 28(2), 157-166.
7. Gray, R. (1984). Vector quantization. IEEE Assp Magazine, 1(2), 4-29.
8. Makhoul, J., Roucos, S., & Gish, H. (1985). Vector quantization inspeech coding. Proceedings of the IEEE, 73(11), 1551-1588.
9. Meagher, D. (1982). Geometric modeling using octree encoding.Computer graphics and image processing, 19(2), 129-147.
10. Uhr, L. (1963). “ Pattern recognition” computers as models for formperception. Psychological Bulletin, 60(1), 40.
11. Li, D., & Wu, M. (2021). Pattern recognition receptors in health anddiseases. Signal transduction and targeted therapy, 6(1), 291.
12. Serey, J., Alfaro, M., Fuertes, G., Vargas, M., Durán, C., Ternero, R., ... &Sabattin, J. (2023). Pattern recognition and deep learning technologies,enablers of industry 4.0, and their role in engineering research.Symmetry, 15(2), 535.
13. Mantel, N., & Brown, C. (1974). Alternative tests for comparing normaldistribution parameters based on logistic regression. Biometrics, 485-497.
14. Kononenko, I. (1989). ID3, sequential Bayes, naive Bayes and Bayesianneural networks. Proc. of European Working Session on LearningEWSL, 4-6.
15. Palmer, E. M., & Schwenk, A. J. (1979). On the number of trees in arandom forest. Journal of Combinatorial Theory, Series B, 27(2), 109-121.
16. Friedman, J. H. (2001). Greedy function approximation: a gradientboosting machine. Annals of statistics, 1189-1232.
17. Fukunaga, K., & Narendra, P. M. (1975). A branch and bound algorithmfor computing k-nearest neighbors. IEEE transactions on computers,100(7), 750-753.
18. Magee, J. F. (1964). Decision trees for decision making (pp. 35-48).Brighton, MA, USA: Harvard Business Review.
19. Vapnik, V., Golowich, S., & Smola, A. (1996). Support vector method forfunction approximation, regression estimation and signal processing.Advances in neural information processing systems, 9.
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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