A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the price of a house based on its square footage, age, number of bedrooms and ...
Pantelis Samartsidis, Claudia R. Eickhoff, Simon B. Eickhoff, Tor D. Wager, Lisa Feldman Barrett, Shir Atzil, Timothy D. Johnson, Thomas E. Nichols Journal of the ...
Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all trades between each counterparty under both market and credit risk. We present a ...
We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a ...
Quantum chemistry uses quantum mechanics for the first-principle exploration of chemical systems. In principle, all chemical phenomena can be studied by solving the Schrödinger equation, the ...
Researchers found that the Gaussian Process Regression (GPR) machine learning model is the most reliable tool for forecasting ...
[Click on image for larger view.] Figure 1: Gaussian Process Regression in Action /figcaption> After training, the model is applied to the training data and the test data. The model scores 97.50 ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback