Kernel Function

Kernel functions are mathematical tools used to define similarity or distance between data points, enabling the application of linear methods to non-linear problems in machine learning and other fields. Current research focuses on developing efficient kernel approximations (e.g., Nyström methods, random features) for large-scale datasets, exploring the properties of kernels in various contexts (e.g., Gaussian processes, neural networks), and designing novel kernels tailored to specific data structures (e.g., graphs, hierarchical data, spherical data). This work has significant implications for improving the scalability, accuracy, and interpretability of machine learning models across diverse applications, including feature selection, regression, classification, and data analysis in various scientific domains.