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A jackknife entropy-based clustering algorithm for probability density functions.
- Source :
-
Journal of Statistical Computation & Simulation . Mar2021, Vol. 91 Issue 5, p861-875. 15p. - Publication Year :
- 2021
-
Abstract
- This paper proposes a new unsupervised learning algorithm called jackknife entropy-based clustering algorithm for grouping families of probability density functions (pdfs). The fitness function is used to choose the best threshold values of similarity in the proposed algorithm. We demonstrate the correctness and robustness of the proposed algorithm on a synthetic data set. Finally, we apply the algorithm to texture clustering. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PROBABILITY density function
*POCKETKNIVES
*ALGORITHMS
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 00949655
- Volume :
- 91
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Journal of Statistical Computation & Simulation
- Publication Type :
- Academic Journal
- Accession number :
- 149381017
- Full Text :
- https://doi.org/10.1080/00949655.2020.1832490