January 1, 2025The International Arab Journal of Information TechnologyOpen Access

Exploring the Intersection of Information Theory and Machine Learning

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Improved classifier accuracy was observed at 95.1% by utilizing mutual information for feature selection, indicating the effectiveness of information theory.
Cross-entropy loss in neural networks led to high accuracy of 0.98 during training, suggesting faster convergence compared to traditional loss metrics.
Application of KL-divergence in structuring latent spaces effectively promoted compact representations in generative models, proving its utility in machine learning.
The information bottleneck principle yielded competitive test accuracy while reducing training time by over 90 seconds, highlighting its role in efficient learning.

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Implication

This analysis demonstrates how information theory enhances feature selection and model training in machine learning, indicating significant performance improvements.

Cite This Study

Jaradat et al. (2025) studied this question.

synapsesocial.com/papers/68af5d63ad7bf08b1eae0972 https://doi.org/10.34028/iajit/22/5/1

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Implication

This analysis demonstrates how information theory enhances feature selection and model training in machine learning, indicating significant performance improvements.

Exploring the Intersection of Information Theory and Machine Learning

Key Points

Discussion

Implication

Cite This Study

Exploring the Intersection of Information Theory and Machine Learning

Key Points

Discussion

Implication

Cite This Study

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