Zobrazeno 1 - 10
of 657
pro vyhledávání: '"Arshad, Mohd"'
Alzheimer's disease (AD) is a leading neurodegenerative condition and the primary cause of dementia, characterized by progressive cognitive decline and memory loss. Its progression, marked by shrinkage in the cerebral cortex, is irreversible. Numerou
Externí odkaz:
http://arxiv.org/abs/2410.14207
Publikováno v:
31st International Conference on Neural Information Processing (ICONIP), 2024
Random vector functional link (RVFL), a variant of single-layer feedforward neural network (SLFN), has garnered significant attention due to its lower computational cost and robustness to overfitting. Despite its advantages, the RVFL network's relian
Externí odkaz:
http://arxiv.org/abs/2410.00510
Publikováno v:
27th International Conference on Pattern Recognition (ICPR), 2024
Twin support vector machine (TSVM), a variant of support vector machine (SVM), has garnered significant attention due to its $3/4$ times lower computational complexity compared to SVM. However, due to the utilization of the hinge loss function, TSVM
Externí odkaz:
http://arxiv.org/abs/2408.16336
Multivariate datasets are common in various real-world applications. Recently, copulas have received significant attention for modeling dependencies among random variables. A copula-based information measure is required to quantify the uncertainty in
Externí odkaz:
http://arxiv.org/abs/2408.02028
Regression analysis is one of the most popularly used statistical technique which only measures the direct effect of independent variables on dependent variable. Path analysis looks for both direct and indirect effects of independent variables and ma
Externí odkaz:
http://arxiv.org/abs/2406.17445
Publikováno v:
REVSTAT-Statistical Journal.(2024)
Dombi et al. (2019) introduced a three parameter omega distribution and showed that its asymptotic distribution is the Weibull model. We propose a new record-based transmuted generalization of the unit omega distribution by considering Balakrishnan a
Externí odkaz:
http://arxiv.org/abs/2405.07958
Publikováno v:
Pattern Recognition, Elsevier (2024)
Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficiency attained by the acquired model. The training of supervised learning
Externí odkaz:
http://arxiv.org/abs/2404.18101
Support vector regression (SVR) has garnered significant popularity over the past two decades owing to its wide range of applications across various fields. Despite its versatility, SVR encounters challenges when confronted with outliers and noise, p
Externí odkaz:
http://arxiv.org/abs/2401.16785
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
In the domain of machine learning, the significance of the loss function is paramount, especially in supervised learning tasks. It serves as a fundamental pillar that profoundly influences the behavior and efficacy of supervised learning algorithms.
Externí odkaz:
http://arxiv.org/abs/2309.02250
Publikováno v:
Statistics & Probability Letters, Volume 206 , March 2024, 109988
We propose a new bivariate symmetric copula with positive and negative dependence properties. The main features of the proposed copula are its simple mathematical structure, wider dependence range compared to FGM copula and its generalizations, and n
Externí odkaz:
http://arxiv.org/abs/2304.02231