"Exploring the Intersection of Self-Regulation and Machine Learning: Enhancing Information Processing Systems Through Adaptive Algorithms"

EasyChair Preprint 14357

Abstract

The integration of self-regulation mechanisms within machine learning frameworks represents a promising frontier in optimizing information processing systems. This paper investigates the intersection of self-regulation and machine learning, focusing on how adaptive algorithms can enhance system performance by dynamically adjusting to varying operational conditions. By examining the principles of self-regulation, including feedback loops and adaptive control, and their application in machine learning models, we propose a novel approach to improving the robustness and efficiency of information processing systems. Through empirical analysis and case studies, we demonstrate how incorporating self-regulatory techniques into machine learning algorithms can lead to more resilient and responsive systems, capable of better handling uncertainties and evolving environments. Our findings suggest that this interdisciplinary approach not only advances theoretical understanding but also offers practical implications for developing more intelligent and adaptive technologies.

Keyphrases: Algorithms, Enhancement, Machine Learning Algorithms, adaptive systems, intersection, self-regulation technique

@booklet{EasyChair:14357,
  author    = {John Owen},
  title     = {"Exploring the Intersection of Self-Regulation and Machine Learning: Enhancing Information Processing Systems Through Adaptive Algorithms"},
  howpublished = {EasyChair Preprint 14357},
  year      = {EasyChair, 2024}}