Machine Learning Cosmic Expansion History
  We use the machine learning techniques, for the first time, to study the
background evolution of the universe in light of 30 cosmic chronometers. From 7
machine learning algorithms, using the principle of mean squared error
minimization on testing set, we find that Bayesian ridge regression is the
optimal method to extract the information from cosmic chronometers. By use of a
power-law polynomial expansion, we obtain the first Hubble constant estimation
$H_0=65.95^{+6.98}_{-6.36}$ km s$^{-1}$ Mpc$^{-1}$ from machine learning. From
the view of machine learning, we may rule out a large number of cosmological
models, the number of physical parameters of which containing $H_0$ is larger
than 3. Very importantly and interestingly, we find that the parameter spaces
of 3 specific cosmological models can all be clearly compressed by considering
both their explanation and generalization abilities.
