Adaptive Learning Density Estimators for Tsallis Entropy and Kapur Entropy with Applications in System Training
Abstract
Adaptation learning is a data-driven technique that gives instructions based on the experiences made during data analysis. It plays an integral role in providing engineering solutions based on specific needs. Researchers have used the second-order statistics criterion for decades to conceptualize the optimality criteria using Shannon and Renyis information-theoretic measures. Some gaps have been identified in this research work, and useful findings have been proved with generalized information-theoretic measures of Renyis as Tsallis entropy of order $\alpha$ and Kapur entropy of order $\alpha$ and type $\beta$ using the Parzen-Rosenblatt window. This work explored the problem of constructing kernel density estimators and their application in adaptive systems training.
Fields of Study & Subjects
Statistical Mechanics and Entropy
Machine Learning and ELM
Neural Networks and Applications
Publication details
- Journal / source
- ICCK Transactions on Machine Intelligence
- Publisher
- Institute of Central Computation and Knowledge Inc.
- Volume
- 2
- Issue
- 1
- Pages
- 12–27
- Publication date
- 6 January 2026