Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Flavio Villanustre"'
Publikováno v:
Journal of Big Data, Vol 11, Iss 1, Pp 1-22 (2024)
Abstract Acquiring labeled datasets often incurs substantial costs primarily due to the requirement of expert human intervention to produce accurate and reliable class labels. In the modern data landscape, an overwhelming proportion of newly generate
Externí odkaz:
https://doaj.org/article/977d85c21b024c07afbcf3f61c5af59b
Publikováno v:
Journal of Big Data, Vol 10, Iss 1, Pp 1-20 (2023)
Abstract Fraud datasets often times lack consistent and accurate labels, and are characterized by having high class imbalance where the number of fraudulent examples are far fewer than those of normal ones. Machine learning designed for effectively d
Externí odkaz:
https://doaj.org/article/ea6e81b0491c4fb792cc9016f1ecfc13
Autor:
Flavio Villanustre, Arjuna Chala, Roger Dev, Lili Xu, Jesse Shaw LexisNexis, Borko Furht, Taghi Khoshgoftaar
Publikováno v:
Journal of Big Data, Vol 8, Iss 1, Pp 1-24 (2021)
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Elec
Externí odkaz:
https://doaj.org/article/e1377e9010cc4cc4bc738608ea59f001
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-22 (2020)
Abstract The increasing reliance on electronic health record (EHR) in areas such as medical research should be addressed by using ample safeguards for patient privacy. These records often tend to be big data, and given that a significant portion is s
Externí odkaz:
https://doaj.org/article/a09ca30997834f4aa4b24c4abb792ff6
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-36 (2019)
Abstract In this paper, we comprehensively explain how we built a novel implementation of the Random Forest algorithm on the High Performance Computing Cluster (HPCC) Systems Platform from LexisNexis. The algorithm was previously unavailable on that
Externí odkaz:
https://doaj.org/article/e88a877a34b74f8281416ae97365a085
Publikováno v:
Journal of Big Data, Vol 6, Iss 1, Pp 1-23 (2019)
Abstract Deep Learning is an increasingly important subdomain of artificial intelligence, which benefits from training on Big Data. The size and complexity of the model combined with the size of the training dataset makes the training process very co
Externí odkaz:
https://doaj.org/article/e0f68ce3a7914c8b8832fe995e7ab6ac
Publikováno v:
Journal of Big Data, Vol 4, Iss 1, Pp 1-17 (2017)
Abstract With the increasing demand for examining and extracting patterns from massive amounts of data, it is critical to be able to train large models to fulfill the needs that recent advances in the machine learning area create. L-BFGS (Limited-mem
Externí odkaz:
https://doaj.org/article/78d94785026c4e50bebacd70e3cf90bf
Autor:
Jesse Shaw LexisNexis, Flavio Villanustre, Borko Furht, Arjuna Chala, Taghi M. Khoshgoftaar, Lili Xu, Roger Dev
Publikováno v:
Journal of Big Data, Vol 8, Iss 1, Pp 1-24 (2021)
Journal of Big Data
Journal of Big Data
This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical En
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::830bfd7a8b9b4874a56a2f2af39548dd
https://doi.org/10.1186/s40537-021-00423-z
https://doi.org/10.1186/s40537-021-00423-z
Publikováno v:
2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS).
Clustering algorithms are an important part of unsupervised machine learning. With Big Data, applying clustering algorithms such as KMeans has become a challenge due to the significantly larger volume of data and the computational complexity of the s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b515a01971516a78ec9ac8b3143988c5
https://doi.org/10.1109/csitss47250.2019.9031047
https://doi.org/10.1109/csitss47250.2019.9031047
Publikováno v:
Journal of Big Data, Vol 4, Iss 1, Pp 1-17 (2017)
With the increasing demand for examining and extracting patterns from massive amounts of data, it is critical to be able to train large models to fulfill the needs that recent advances in the machine learning area create. L-BFGS (Limited-memory Broyd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0a55fa421fab8e5b5329db8f8099bf8
http://link.springer.com/article/10.1186/s40537-017-0084-5
http://link.springer.com/article/10.1186/s40537-017-0084-5