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In this paper, depending on different tasks (dimension reduction and similarity metric learning), GP-Metric involves two main steps to predict the new test samples: the feature learning step and the similarity prediction step. In the feature learning step, our goal is to learn the latent variable for a new sample .
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Among many modeling choices, this paper is mainly focused on advancing the robust regression approaches for a Gaussian process (GP) regression modeling. The GP ...
Abstract. Learning appropriate distance metric from data can significantly improve the performance of machine learning tasks under investigation.
... The Gaussian process (GP) model is a statistical model that defines a distribution over functions, embodying a collection of random variables where any ...
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 ...
Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems.
We approach the problem from a discriminative perspective by learning, jointly over the training data, the target space variance in the neighborhood of a ...
The overall training procedure of the dense Gaussian process model is summarized in Algorithm 1. ... Metric learning [32] has been explored for few-shot learning ...
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We use this insight to define a Gaussian process model of human function learning that combines the strengths of both approaches. 1 Introduction. Much research ...