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.
Code for the paper "Gaussian Process Classification as Metric Learning for Forensic Writer Identification", published at DAS 2018 - fredrikwahlberg/das2018.
We propose unsupervised (distance-based) algorithms for finding suitable replacements. We also apply several standard classification techniques. This paper is ...
Apr 19, 2024 · can be constructed using observational measurements without presupposing a particular form. This reconstruction process, known as the Gaussian ...
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems.
Metric — Gaussian Process trained through metric learning for esti- mating the diagonal covariance matrix — with the scores obtained when using the ranking ...
Aug 15, 2018 · In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed. An unsupervised ...