Kernel Extreme Learning Machine

Kernel Extreme Learning Machines (KELMs) are a type of machine learning algorithm designed for fast and efficient classification and regression tasks, leveraging kernel methods to handle non-linear data. Current research focuses on enhancing KELM performance through architectural improvements, such as multi-column architectures to address the curse of dimensionality in large datasets, and by integrating it with other techniques like feature extraction methods (e.g., from CNNs or spectral analysis) and optimization algorithms (e.g., sparrow search) for improved accuracy and generalization. These advancements are proving valuable in diverse applications, including anomaly detection in UAV data, ADHD diagnosis from brain imaging, and food detection in images, demonstrating KELM's versatility and potential for real-world impact.