Kernel Selection

Kernel selection is the crucial process of choosing the optimal kernel function for machine learning algorithms, significantly impacting model performance and efficiency. Current research focuses on automating kernel selection, moving away from manual or heuristic approaches, through methods like marginal likelihood maximization and data-dependent regret analysis within online learning frameworks. This optimization is vital across various applications, including causal discovery, convolutional neural networks (CNNs) for image processing, and support vector machines (SVMs), improving accuracy, reducing computational cost, and enhancing model generalization. The development of efficient and data-driven kernel selection techniques is thus a key area for advancing machine learning capabilities.