Ensemble Approach to Building Mercer Kernels

The results show that the Mixture Density Mercer Kernel described here outperforms tree-based classification in distinguishing high-redshift galaxies from low redshift galaxies by approximately 16% on test data, bagged trees by approximately 7%, and bagged trees built on a much larger sample of data by approximately 2%. The code for these experiments has been generated with the AutoBayes tool, which automatically generates efficient and documented C/C++ code from abstract statistical model specifications.

The core of the system is a schema library which contains templates for learning and knowledge discovery algorithms like different versions of EM, or numeric optimization methods like conjugate gradient methods. The template instantiation is supported by symbolic algebraic computations, which allows AutoBayes to find closed form solutions and, where possible, to integrate them into the code.