Zobrazeno 1 - 10
of 894
pro vyhledávání: '"Conformal prediction"'
Publikováno v:
BMC Bioinformatics, Vol 25, Iss 1, Pp 1-18 (2024)
Abstract Background Graphical representations are useful to model complex data in general and biological interactions in particular. Our main motivation is the comparison of metabolic networks in the wider context of developing noninvasive accurate d
Externí odkaz:
https://doaj.org/article/36b5f1d4faee491c9b4c96463c1595b6
Autor:
Geethen Singh, Glenn Moncrieff, Zander Venter, Kerry Cawse-Nicholson, Jasper Slingsby, Tamara B. Robinson
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Machine learning is increasingly applied to Earth Observation (EO) data to obtain datasets that contribute towards international accords. However, these datasets contain inherent uncertainty that needs to be quantified reliably to avoid nega
Externí odkaz:
https://doaj.org/article/3e3cb4badbb7445394bfe012fa5182e9
Autor:
Chuanjun Xu, Qinmei Xu, Li Liu, Mu Zhou, Zijian Xing, Zhen Zhou, Danyang Ren, Changsheng Zhou, Longjiang Zhang, Xiao Li, Xianghao Zhan, Olivier Gevaert, Guangming Lu
Publikováno v:
European Journal of Radiology Open, Vol 13, Iss , Pp 100603- (2024)
Purpose: The novel coronavirus pneumonia (COVID-19) has continually spread and mutated, requiring a patient risk stratification system to optimize medical resources and improve pandemic response. We aimed to develop a conformal prediction-based tri-l
Externí odkaz:
https://doaj.org/article/c18272aff51b4df1a687f4c510bb6b87
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract The application of machine learning (ML) to solve real-world problems does not only bear great potential but also high risk. One fundamental challenge in risk mitigation is to ensure the reliability of the ML predictions, i.e., the model err
Externí odkaz:
https://doaj.org/article/e4cf8136b79944fc88cae05c829f2352
Autor:
Jorge De la Torre, Leticia R. Rodriguez, Francisco E. L. Monteagudo, Leonel R. Arredondo, José B. Enriquez
Publikováno v:
Energy Science & Engineering, Vol 12, Iss 3, Pp 524-540 (2024)
Abstract In recent years, machine and deep learning models have attracted significant attention for electricity price forecast in global wholesale electricity markets. Yet, a predominant focus on point forecast in most parts of literature limits the
Externí odkaz:
https://doaj.org/article/6d9b110873c846178741555af9da4d07
Publikováno v:
IEEE Access, Vol 12, Pp 165626-165652 (2024)
Alarm flood management is essential for industrial process plant safety and efficiency. Online “alarm flood classification” (AFC) assigns an observed sequence of alarms to one (of many) alarm flood classes known from the past. Nevertheless, accur
Externí odkaz:
https://doaj.org/article/0ddec154ade64ea6b34523f77a990cb5
Publikováno v:
IEEE Access, Vol 12, Pp 109847-109860 (2024)
Today, there exists a large number of different embedded hardware platforms for accelerating the inference of Deep Neural Networks (DNNs). To enable rapid application development, a number of prediction frameworks have been proposed to estimate the D
Externí odkaz:
https://doaj.org/article/932c1e0df8954c7c99e4b0f2d7845356
Publikováno v:
IEEE Access, Vol 12, Pp 53579-53597 (2024)
Machine learning often lacks transparent performance indicators, especially in generating point predictions. This paper addresses this limitation through conformal prediction, a non-parametric forecasting technique seamlessly integrating with regress
Externí odkaz:
https://doaj.org/article/6a86c06fb8794437a782e67c6da96dcf
Publikováno v:
Stats, Vol 7, Iss 1, Pp 23-33 (2024)
Perhaps the first nonparametric, asymptotically optimal prediction intervals are provided for univariate random walks, with applications to renewal processes. Perhaps the first nonparametric prediction regions are introduced for vector-valued random
Externí odkaz:
https://doaj.org/article/e9e3d014108c465f91048eb1e3907f12
Autor:
Sangwoo Park, Osvaldo Simeone
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 5, Pp 1-24 (2024)
Quantum machine learning is a promising programming paradigm for the optimization of quantum algorithms in the current era of noisy intermediate-scale quantum computers. A fundamental challenge in quantum machine learning is generalization, as the de
Externí odkaz:
https://doaj.org/article/fa98064aded04f6da16d8b44a1af9f05