scholar.google.com › citations
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 ...
People also ask
What is the Gaussian process learning model?
What is the Gaussian model of machine learning?
What are Gaussian processes used for in machine learning?
What is the Gaussian process technique?
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 regression ... 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. ... All Gaussian process kernels are ...
We approach the problem from a discriminative perspective by learning, jointly over the training data, the target space variance in the neighborhood of a ...
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.
... 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 ...