A Kernel for Hierarchical Parameter Spaces

This work addresses a foundational issue in machine learning for researchers and practitioners dealing with complex parameter spaces, though it appears incremental as it builds on existing kernel methods.

The authors tackled the problem of defining kernels for mixed continuous/discrete hierarchical parameter spaces by introducing a family of such kernels and proving their positive definiteness.