Results 121 to 130 of about 110,828 (307)

Gaussian process regression analysis for functional data

, 2011
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space.
Shi, Jian Qing, Choi, Taeryon, Jian Qing Shi, Taeryon Choi   +3 more
core   +1 more source

Link quality prediction model based on Gaussian process regression

Tongxin xuebao, 2018
Link quality is an important factor of reliable communication and the foundation of upper protocol design for wireless sensor network.Based on this,a link quality prediction model based on Gaussian process regression was proposed.It employed grey ...
Jian SHU, Manlan LIU, Yaqing SHANG, Yubin CHEN, Linlan LIU   +4 more
doaj   +2 more sources

Benchmarking of quantum fidelity kernels for Gaussian process regression

Machine Learning: Science and Technology
Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest.
Xuyang Guo, Jun Dai, Roman V Krems
doaj   +1 more source

Conductive Hydrogels for Exogenous Sensing and Cell Fate Control

Advanced Materials, EarlyView.
We engineer electrically conductive hydrogels by combining sulfated glycosaminoglycans with semiconducting polymers. These hydrogels bind bioactive proteins, including growth factors, whose release or retention can be modulated by low‐voltage stimulation. The hydrogels are also integrated as 3D channels in organic electrochemical transistors as part of
Teuku Fawzul Akbar, Carlos Alejandro Jimenez‐Rodriguez, Railia Biktimirova, Ilka Hermes, Thomas Kurth, My Duyen Pham, Mikhail Tsurkan, Jens Friedrichs, Francis L. C. Morgan, Hans Kleemann, Olga Guskova, Uwe Freudenberg, Peter Fratzl, Carsten Werner, Christoph Tondera, Ivan R. Minev   +15 more
wiley   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

Advanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung, Jiaxiang Yi, Hieu Nguyen‐Duc, Tien Nu Hoang Lo, Weili Zhao, Chaoqun Dong, Fabien Sorin, Baris Caglar, Lei Wei, Tung Nguyen‐Dang   +9 more
wiley   +1 more source

Gaussian process regression predictor example.

, 2018
Gaussian process regression predictor example.
Jacco M. Hoekstra (5815076), Joost Ellerbroek (5815073), Junzi Sun (5815067), Huy Vû (5815070)   +3 more
core   +1 more source

Multivariate Interpolation of Wind Field Based on Gaussian Process Regression

Atmosphere, 2018
The resolution of the products of numerical weather prediction is limited by the resolution of numerical models and computing resources, which can be improved accurately by a well-chosen interpolation algorithm.
Miao Feng, Weimin Zhang, Xiangru Zhu, Boheng Duan, Mengbin Zhu, De Xing   +5 more
doaj   +1 more source

Gaussian Process Regression with Mismatched Models [PDF]

, 2002
7 pages, style file nips01e.sty ...
openaire   +3 more sources

Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction

Advanced Materials, EarlyView.
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano, Zihan You, Haoran Chen, Qifan Zhong, Rong‐Zhen Liao, Shaoqi Zhan   +5 more
wiley   +1 more source

Gaussian Process Regression with Measurement Error

IEICE Transactions on Information and Systems, 2010
Regression analysis that incorporates measurement errors in input variables is important in various applications. In this study, we consider this problem within a framework of Gaussian process regression. The proposed method can also be regarded as a generalization of kernel regression to include errors in regressors.
Yukito Iba, Shotaro Akaho
openaire   +2 more sources