Zobrazeno 1 - 10
of 91
pro vyhledávání: '"Shen Shyang Ho"'
Publikováno v:
Information Sciences. 595:395-426
Publikováno v:
International Conference on Cyber Warfare and Security. 17:469-478
The persistent shortage of cybersecurity professionals combined with enterprise networks tasked with processing more data than ever before has led many cybersecurity experts to consider automating some of the most common and time-consuming security t
Autor:
Steven E. Arroyo, Shen Shyang Ho
Publikováno v:
2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA).
Publikováno v:
Database Systems for Advanced Applications ISBN: 9783031001222
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6170665a2047169c8115e2ac589ab2e
https://doi.org/10.1007/978-3-031-00123-9_40
https://doi.org/10.1007/978-3-031-00123-9_40
Publikováno v:
2021 IEEE Global Communications Conference (GLOBECOM).
Publikováno v:
ITSC
Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, particularly when there is a spike in user demand at certain locations or an increase in number of vehicles due to special events. If such imbalances are
Autor:
Hieu D. Nguyen, Mohammed Sarosh Khan, Nicholas Kaegi, Shen-Shyang Ho, Jonathan Moore, Logan Borys, Lucas Lavalva
New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectivene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b2e78a46ee305128866f7bef71f78e6
http://arxiv.org/abs/2109.08967
http://arxiv.org/abs/2109.08967
Publikováno v:
2021 IEEE International Conference on Imaging Systems and Techniques (IST).
Autor:
Shen-Shyang Ho, Jianjun Zhao
Publikováno v:
International Journal of Approximate Reasoning. 107:101-121
Learning the structure of a Bayesian network (BN) from data is NP-hard. To efficiently handle high-dimensional datasets, many BN local structure learning algorithms are proposed. These learning algorithms can be categorized into two types: constraint
Publikováno v:
Machine Learning. 108:809-830
A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-cl