This paper proposes a multiple instance learning (MIL) algorithm for Gaussian pro- cesses (GP). The GP-MIL model inherits two crucial benefits from GP: (i) ...
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PDF | On Apr 1, 2018, Fredrik Wahlberg published Gaussian Process Classification as Metric Learning for Forensic Writer Identification | Find, read and cite ...
Gaussian Processes for Machine Learning presents one of the most important. Bayesian machine learning approaches ... metric positive semidefinite, its ...
Any model that is linear in its parameters with a Gaussian distribution over the parameters is a Gaussian process. This class spans discrete models, including ...
We present a novel method for learning with Gaussian process regres- sion in a hierarchical Bayesian framework. In a first step, kernel matri- ces on a ...
Jul 10, 2023 · measure, which allows us to develop an episodic approach for learning a GP model ensuring a specified tracking error bound. This article is ...
Aug 15, 2018 · The metric learning inference was based on multiclass Gaussian process classification. Using the popular datasets IAM and CVL combined, the ...
Reachability-based safe learning with Gaussian processes - IEEE Xplore
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We propose a novel method that uses a principled approach to learn the system's unknown dynamics based on a Gaussian process model and iteratively approximates ...
(2) Scalability: Gaussian processes, for example, scale as O(n^3) for training, O(n^2) for storage, and O(n^2) per test prediction, for n training points. This ...
The efficient adaptation to the GP prior through updating the dense inducing variables enables the proposed method with improved generalization to novel tasks ...
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