Results 61 to 70 of about 105,151 (260)

Gaussian process regression‐based load forecasting model

IET Generation, Transmission & Distribution
In this paper, Gaussian Process Regression (GPR)‐based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed.
Anamika Yadav, Rashmi Bareth, Matushree Kochar, Mohammad Pazoki, Ragab A. El Sehiemy   +4 more
doaj   +1 more source

Reinforcement learning with Gaussian process regression using variational free energy

Journal of Intelligent Systems, 2023
The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm.
Kameda Kiseki, Tanaka Fuyuhiko
doaj   +1 more source

Implicit Manifold Gaussian Process Regression

Advances in Neural Information Processing Systems 36, 2023
Gaussian process regression is widely used because of its ability to provide well-calibrated uncertainty estimates and handle small or sparse datasets. However, it struggles with high-dimensional data. One possible way to scale this technique to higher dimensions is to leverage the implicit low-dimensional manifold upon which the data actually lies, as
Fichera, Bernardo, Borovitskiy, Viacheslav, Krause, Andreas, Billard, Aude   +3 more
openaire   +5 more sources

Stage‐Dependent β‐Synuclein Links MRI and Cognitive Decline in Alzheimer's Disease

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Synaptic degeneration drives cognitive decline in Alzheimer's disease (AD), but synaptic biomarkers are scarce. Brain‐enriched β‐synuclein emerged as a synaptic damage marker. We investigated its diagnostic, prognostic, and structural correlates across the AD continuum.
Ulaş Ay, Merve Alaylioglu, Erdi Sahin, Sanem Sultan Yoruk Oner, Gulce Iskan, Gulce Cosku Yilmaz Cakan, Gozde Kizilates‐Evin, Emre Hari, Elif Kurt, Erdinc Dursun, Basar Bilgic, Hasmet Hanagasi, Hakan Gurvit, Tamer Demiralp, Duygu Gezen‐Ak, Bedia Samanci   +15 more
wiley   +1 more source

Localization Reliability Improvement Using Deep Gaussian Process Regression Model

Sensors, 2018
With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor ...
Fei Teng, Wenyuan Tao, Chung-Ming Own
doaj   +1 more source

Noise Estimation in Gaussian Process Regression

CoRR, 2022
We develop a computational procedure to estimate the covariance hyperparameters for semiparametric Gaussian process regression models with additive noise. Namely, the presented method can be used to efficiently estimate the variance of the correlated error, and the variance of the noise based on maximizing a marginal likelihood function.
Siavash Ameli, Shawn C. Shadden
openaire   +3 more sources

White Matter and Perivascular Imaging Changes in Alzheimer's Disease and Cerebral Amyloid Angiopathy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Peak‐width of skeletonized mean diffusivity (PSMD) and diffusion tensor imaging–analysis along the perivascular space (DTI‐ALPS), reflecting white matter integrity and glymphatic function, are altered in Alzheimer's disease (AD).
Debina Laishram, Guangwei Du, Sangam Kanekar, Nandakumar Nagaraja   +3 more
wiley   +1 more source

Gaussian Process Regression´s Hyperparameters Optimization to Predict Financial Distress

Retos, 2023
Predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology.
Jakub Horak, Amine Sabek
doaj   +1 more source

Privacy-Aware Gaussian Process Regression

Technometrics
We propose a novel theoretical and methodological framework for Gaussian process regression subject to privacy constraints. The proposed method can be used when a data owner is unwilling to share a high-fidelity supervised learning model built from their data with the public due to privacy concerns.
Tuo, Rui, Chen, Haoyuan, Bhattacharya, Raktim   +2 more
openaire   +3 more sources

Diffusion Basis Spectrum Imaging of Tissue Microstructure and Early Treatment in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Conventional MRI cannot resolve the coexisting pathological processes within multiple sclerosis tissue. Diffusion Basis Spectrum Imaging decomposes the diffusion signal into fiber, cell, and hindered ratios indexing axonal integrity, cellularity, and extracellular space, respectively.
Apisit Kaewsanit, Aime Luna, Bradley Judge, Nicole Shelley, Karl Friedrichsen, Jayashree Rajamanickam, Zhude Tu, Yong Wang, Tammie L. S. Benzinger, Qing Wang, Matthew R. Brier   +10 more
wiley   +1 more source