HPCC Systems has a robust Academic Program that collaborates with colleges, universities, high schools and institutions of higher learning around the world.
One such collaboration with the Rashtreeya Vidyalaya College of Engineering (RVCE) in Bengaluru, India, has resulted in a new publication:
Gaussian process regression is a powerful machine learning method to solve non-linear regression problems. However, because of the intensive computation, Gaussian process regression is not suitable for large-scale machine learning problems. Fortunately, researchers developed approximation methods to get a solution arbitrarily close as the original Gaussian process more rapidly and with better scaling. In this post, a Random Fourier Features accelerated Gaussian process regressor will be introduced.