propose a non-parametric metric learning approach (GP-Metric) based on Gaussian Process (GP). •. use GP to extend the bilinear similarity into a non ...

To the best of our knowledge, this is the first work that directly uses GP as non-parametric metric. In the experiments, we compare our approach with related GP ...

Gaussian process approach for metric learning ... Abstract. Learning appropriate distance metric from data can significantly improve the performance of machine ...

We algorithmically construct multi-output Gaussian process priors which satisfy linear differential equations. Our approach attempts to parametrize all ...

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 ... It contrasts sharply with the popular metric-based meta-learning approach ...

It allows to give a probabilistic approach to prediction by giving the mean and standard deviation as output when predicting. ... Rasmussen and Christopher K.I. ...

This approach to prediction uses a Gaussian process, a stochastic process that induces a Gaussian distribution on y based on the values of x. This approach.

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

... approach. for easier use in the Gaussian process based learning of an affinity. metric (w.r.t. the separation of writer hands). The main reasons for using GPC ...