Techniques and optimization algorithms in machine learning: A review

Abstract

Machine learning (ML) has transformed different sectors by allowing for data-based decision-making and forecasting analysis. This study explores the most recent techniques and algorithms that are influencing the direction of machine learning. The research investigates different supervised learning techniques, such as advanced versions of decision trees, support vector machines, and ensemble methods like XGBoost and random forests. Unsupervised learning involves the study of clustering algorithms like k-means++, hierarchical clustering, and density-based spatial clustering of applications with noise (DBSCAN), which are used for anomaly detection and customer segmentation. An overview of convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) and their advanced form, long short-term memory (LSTM) networks, for time-series analysis and natural language processing address deep learning, a subset of ML. The study highlights the progress of generative adversarial networks (GANs) and transformer models, showcasing notable improvements in generative tasks and language models, respectively. Moreover, the research delves into reinforcement learning, with an emphasis on recent advancements in deep reinforcement learning and how it is utilized in autonomous systems and playing games. Also covered are new developments like federated learning, which tackles data privacy issues by allowing ML models to be trained on decentralized devices, and quantum machine learning, which uses quantum computing to improve algorithm performance. This thorough review is designed to give a complete understanding of modern ML techniques and algorithms, providing insights into their practical use and potential for future innovation in different industries.

Topics & Concepts