Results 261 to 270 of about 731,480 (301)

Divisive Gaussian Processes for Nonstationary Regression

IEEE Transactions on Neural Networks and Learning Systems, 2014
Standard Gaussian process regression (GPR) assumes constant noise power throughout the input space and stationarity when combined with the squared exponential covariance function. This can be unrealistic and too restrictive for many real-world problems.
Luis Muñoz-González, Miguel Lázaro-Gredilla, Aníbal R Figueiras-Vidal   +2 more
exaly   +3 more sources

A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression

Journal of Cleaner Production, 2018
Debin Fang, Xiaoling Zhang, Qian Yu, Trenton Chen Jin, L. Tian   +4 more
semanticscholar   +3 more sources

Bearing degradation assessment and remaining useful life estimation based on Kullback-Leibler divergence and Gaussian processes regression

, 2021
Accurate monitoring of degradation in bearing is essential for preventing unexpected shutdown of a machinery system. This paper proposes a novel health degradation indicator for machineries, based on a Kullback-Leibler divergence.
P. Kumar, L. Kumaraswamidhas, S. K. Laha
semanticscholar   +1 more source

Robust and Conjugate Gaussian Process Regression

International Conference on Machine Learning, 2023
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed Gaussian observation noise.
Matias Altamirano, F. Briol, Jeremias Knoblauch   +2 more
semanticscholar   +1 more source

Probabilistic Deep Ordinal Regression Based on Gaussian Processes

IEEE International Conference on Computer Vision, 2019
With excellent representation power for complex data, deep neural networks (DNNs) based approaches are state-of-the-art for ordinal regression problem which aims to classify instances into ordinal categories.
Yanzhu Liu, Fan Wang, A. Kong
semanticscholar   +1 more source

Bounded Gaussian process regression

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework.
Bjorn Sand Jensen, Jens Brehm Nielsen, Jan Larsen   +2 more
openaire   +1 more source

Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression

IEEE Transactions on Control Systems Technology, 2019
Gaussian mixture regression (GMR) is an effective tool in developing soft sensors for online estimating difficult-to-measure variables in industrial processes with multiple operating modes. However, the GMR usually requires a sufficient amount of labeled
Weiming Shao, Zhiqiang Ge, Zhihuan Song
semanticscholar   +1 more source

Sparse inverse kernel Gaussian Process regression

Statistical Analysis and Data Mining: The ASA Data Science Journal, 2013
AbstractRegression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression (GPR) is a popular technique for modeling the input–output relations of a set of variables under the assumption that the weight vector has a Gaussian prior.
Das, Kamalika, Srivastava, Ashok N.
openaire   +2 more sources

Gaussian processes for dynamics learning in model predictive control

Annual Reviews in Control
Due to its state-of-the-art estimation performance complemented by rigorous and non-conservative uncertainty bounds, Gaussian process regression is a popular tool for enhancing dynamical system models and coping with their inaccuracies.
A. Scampicchio, Elena Arcari, Amon Lahr, M. Zeilinger   +3 more
semanticscholar   +1 more source

Recursive Gaussian process regression

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
For large data sets, performing Gaussian process regression is computationally demanding or even intractable. If data can be processed sequentially, the recursive regression method proposed in this paper allows incorporating new data with constant computation time.
openaire   +1 more source