Multiple instance learning (MIL) is a binary classification problem with loosely supervised data where a class label is assigned only to a bag of instances indicating presence/absence of positive instances. In this paper we introduce a novel MIL algorithm using Gaussian processes (GP). The bag ...
The security of Gaussian quantum networks can be further extended by considering finite-size correction terms dependent on small failure probabilities of different processes of the protocol. Over a chosen route of the network, Alice and Bob would share the following classical-quantum state between them...
Gaussian processes for machine learning MIT Press (2006) Google Scholar [31] Frazier P.I. A tutorial on Bayesian optimization (2018), pp. 1-22 arXiv arXiv:1807.02811 CrossrefGoogle Scholar [32] Rojas-Gonzalez S., Van Nieuwenhuyse I. A survey on kriging-based infill algorithms for multi...
Python3 project applying Gaussian process regression for forecasting stock trends - gdroguski/GaussianProcesses
To provide a unified framework for resolving such problems, this work employs and modifies Gaussian processes (GPs) (see [52], [30]), which is a non-parametric Bayesian machine learning technique. Quoting Diaconis [13], “once we allow that we don't know f (and u), but do know some...
Using machine learning to predict event times can potentially address these limitations. Traditional supervised learning methods such as logistic regression, random forest, and naive Bayes are suboptimal for modeling longitudinal processes as they cannot account for intertemporal associations in either outcomes...
For instance, the recently developed Gaussian Splatting depends heavily on the accuracy of SfM-derived points and poses. However, SfM processes are time-consuming and often prove unreliable in sparse-view scenarios, where matched features are scarce, leading to accumulated errors and limited ...
. Also, less experiencedpractitioners can benefit from query-by-semantic methods in training processes, especially for difficult-to-interpretcases with multiple pathologies. In this article we develop a methodology for ranking medical images using cus-tomized mixture models. The regions of interest are...
Furthermore, the data set in question is functional and thus the analyses are embedded in Gaussian assumptions and observations treated as Gaussian processes as opposed to individual points. This is where Gaussian tree constraints can provide useful insight into the difficult question of tree-...
Gaussian Processes (GPs) [12] are a powerful tool for Bayesian nonlinear regression. When combined in mixture models, GPs can be applied to describe data when there are local non-stationarities or discontinuities [13], [14], [15], [16]. The components of the mixture model are GPs and ...