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.

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.

Apr 19, 2024 · Numerous modified gravity theories have been proposed in the literature, with a recent emphasis on a theory rooted in non-metric scalar Q Q Q ...

In this paper, a statistical machine learning approach for constructing a metric separating unseen writer hands, is proposed.

This work incorporates the multi-modality of the data distribution into a Gaussian Process regression model. We approach the problem from a discriminative ...

We present a novel method for learning with Gaussian process regres- sion in a hierarchical Bayesian framework. ... We evaluate our approach as a recommendation ...

PDF | On Apr 1, 2018, Fredrik Wahlberg published Gaussian Process Classification as Metric Learning for Forensic Writer Identification | Find, read and cite ...