Types of Machine Learning Focused on Hybrid Learning Algorithms

Machine learning has emerged as a powerful discipline that allows computers to learn autonomously from data without explicit programming instructions. This presentation provides an overview of machine learning, including its connection to artificial intelligence, and explores 14 types of learning techniques. The article covers supervised learning, where models establish relationships between inputs and target variables, and unsupervised learning, which identifies patterns within data without explicit guidance. Additionally, hybrid learning problems such as semi-supervised learning, self-supervised learning, and multi-instance learning are discussed, each addressing unique data scenarios. The concept of ensemble learning, which combines multiple machine learning models, is introduced, highlighting the benefits of aggregating predictions from weak learners. The article further explores the concept of hybrid machine learning (HML), where multiple algorithms are combined to achieve superior performance. Three approaches to HML are presented: architecture integration, information control, and model parameter optimization. Overall, this article provides a comprehensive understanding of machine learning techniques and the growing importance of hybrid algorithms in tackling complex learning problems.