To meet this, we use Gaussian Process (GP) to extend the bilinear similarity into a non-parametric metric (here we abuse the concept of metric) and then learn ...
Abstract. Learning appropriate distance metric from data can significantly improve the performance of machine learning tasks under investigation.
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The main challenge of our work concerns the formulation and learning of non-parametric distance metric. To meet this, we use Gaussian Process (GP) to extend the ...
In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
This paper presents Gaussian process meta-learning (GPML) for few-shot regression, which explicitly exploits the distance between regression problems/tasks ...
In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.
Code for the paper "Gaussian Process Classification as Metric Learning for Forensic Writer Identification", published at DAS 2018 - fredrikwahlberg/das2018.
Apr 18, 2024 · In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process ...
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems.
PDF | On Apr 1, 2018, Fredrik Wahlberg published Gaussian Process Classification as Metric Learning for Forensic Writer Identification | Find, read and cite ...