Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Liangxiao Jiang"'
Publikováno v:
Mathematics, Vol 9, Iss 19, p 2378 (2021)
Due to its simplicity, efficiency, and effectiveness, multinomial naive Bayes (MNB) has been widely used for text classification. As in naive Bayes (NB), its assumption of the conditional independence of features is often violated and, therefore, red
Externí odkaz:
https://doaj.org/article/2e97697dbd76449999976e055cf9edb2
Publikováno v:
Entropy, Vol 19, Iss 9, p 501 (2017)
Of numerous proposals to improve the accuracy of naive Bayes by weakening its attribute independence assumption, semi-naive Bayesian classifiers which utilize one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth
Externí odkaz:
https://doaj.org/article/e189ffa402e440dab616c051736b4a9e
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 17:1-18
In crowdsourcing scenarios, we can obtain each instance’s multiple noisy labels set from different crowd workers and then use a ground truth inference algorithm to infer its integrated label. Despite the effectiveness of ground truth inference algo
Publikováno v:
Information Fusion. 91:529-541
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:6558-6568
Crowdsourcing services provide a fast, efficient, and cost-effective way to obtain large labeled data for supervised learning. Unfortunately, the quality of crowdsourced labels cannot satisfy the standards of practical applications. Ground-truth infe
Publikováno v:
Information Sciences. 606:397-409
Publikováno v:
International Journal of Machine Learning and Cybernetics.
Publikováno v:
Knowledge and Information Systems. 64:2123-2140
Autor:
Huan Zhang, Liangxiao Jiang
Publikováno v:
Neurocomputing. 488:402-411
Publikováno v:
Applied Intelligence. 52:17784-17796