Navigating Complexity: Advanced Optimization Techniques for Machine Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: 1 November 2024 | Viewed by 2476
Special Issue Editors
Interests: computational modelling; deep learning; soft computing; performance analysis; uncertainty and indeterminacy; operations management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancements in machine learning have brought forth complex challenges that necessitate equally advanced optimization techniques. As machine learning finds applications in diverse sectors such as healthcare, finance, and autonomous systems, the need for optimized algorithms becomes crucial. Traditional optimization methods often fall short in navigating the high-dimensionality, non-convexity, and real-time requirements of modern machine learning problems. This Special Issue aims to explore the frontier of optimization techniques designed to address these complexities in machine learning applications. It will feature contributions that present innovative algorithms, theoretical insights, and real-world applications to accelerate and refine machine learning models. Potential topics include, but are not limited to: advanced gradient descent variants in machine learning, Smith-objective optimization for hyperparameter tuning, meta-learning for algorithmic optimization, Bayesian optimization in machine learning, optimization under uncertainty and indeterminacy in machine learning, soft computing approaches for machine learning optimization, scalability challenges in machine learning optimization, real-world applications of optimized machine learning algorithms, performance analysis of new optimization techniques in machine learning, and convergence rates and acceleration methods in optimization for machine learning.
Dr. Seyyed Ahmad Edalatpanah
Dr. Mohammad Javad Ebadi
Guest Editors
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Keywords
- machine learning optimization
- advanced gradient descent
- multi-objective optimization
- Bayesian optimization
- soft computing
- scalability in optimization
- convergence and acceleration techniques
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