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GP: A Gaussian Process regressor that models a function using a kernel. It updates with new data and makes predictions with associated uncertainty (mean and variance). ###3 Data Generator Class.
In this notebook, we demonstrate the use of Gaussian Processes (GPs) from Scikit-learn Gaussian Process Regression . Designing a Kernel and Applying it in SVM for NLP Tasks: In this notebook, we show ...
To address these issues, we propose MetaGP, a meta-learning-based Gaussian process latent variable model that uses a Gaussian process kernel function to capture long-term dependencies and to maintain ...
The current work introduces a novel combination of two Bayesian tools, Gaussian Processes (GPs), and the use of the Approximate Bayesian Computation (ABC) algorithm for kernel selection and parameter ...
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are learned from data ...
This paper revisits single-channel audio source separation based on a probabilistic generative model of a mixture signal defined in the continuous time domain. We assume that each source signal ...
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