John Moeller

Machine Learning Researcher

Continuous Kernel Learning

2016-09 ECMLPKDD Riva di Garda, Italy
Author(s): John Moeller, Vivek Srikumar, Sarathkrishna Swaminathan, Suresh Venkatasubramanian, and Dustin Webb

Kernel learning is the problem of determining the best kernel (either from a dictionary of fixed kernels, or from a smooth space of kernel representations) for a given task. In this paper, we describe a new approach to kernel learning that establishes connections between the Fourier-analytic representation of kernels arising out of Bochner's theorem and a specific kind of feed-forward network using cosine activations. We analyze the complexity of this space of hypotheses and demonstrate empirically that our approach provides scalable kernel learning superior in quality to prior approaches.

About

I defended my PhD thesis in Computer Science at the University of Utah School of Computing, under Suresh Venkatasubramanian. My specialties are in machine learning and algorithms. I am no longer on the market! I'll be joining Kitware in Fall of 2016.