Kernel Extension

Kernel extension encompasses methods that enhance existing systems or algorithms by incorporating kernel functions, which map data into higher-dimensional spaces to reveal non-linear relationships. Current research focuses on improving kernel methods' efficiency and applicability across diverse domains, including machine learning models (e.g., Gaussian processes, support vector machines, neural networks), system identification, and signal processing, often employing techniques like random features and kernel normalization to address scalability issues. This work is significant for advancing machine learning capabilities, improving the performance of various systems, and enabling new approaches to data analysis in fields ranging from genomics to materials science.