Results 21 to 30 of about 392,453 (267)
MedEdPORTAL, 2010 This web-based learning platform was developed for use by faculty and students who wish to learn about and engage in mentor/mentee relationships. Mentoring is a learning process where helpful, personal, and reciprocal relationships are built while ...
Kalyani Premkumar, Angie Wong
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Communication-Efficient Distributed Learning for High-Dimensional Support Vector Machines
Mathematics, 2022 Distributed learning has received increasing attention in recent years and is a special need for the era of big data. For a support vector machine (SVM), a powerful binary classification tool, we proposed a novel efficient distributed sparse learning ...
Xingcai Zhou, Hao Shen
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Distributional Learning of Appearance
PLoS ONE, 2013 Opportunities for associationist learning of word meaning, where a word is heard or read contemperaneously with information being available on its meaning, are considered too infrequent to account for the rate of language acquisition in children. It has been suggested that additional learning could occur in a distributional mode, where information is ...
Griffin, LD, Wahab, MH, Newell, AJ
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Deep Learning-Based Efficient Analysis for Encrypted Traffic
Applied Sciences, 2023 To safeguard user privacy, critical Internet traffic is often transmitted using encryption. While encryption is crucial for protecting sensitive information, it poses challenges for traffic identification and poses hidden dangers to network security.
Xiaodan Yan
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Distributed learning in the presence of disturbances [PDF]
2016 European Control Conference (ECC), 2016 We consider a problem where multiple agents must learn an action profile that maximises the sum of their utilities in a distributed manner. The agents are assumed to have no knowledge of either the utility functions or the actions and payoffs of other agents.
Chithrupa Ramesh +2 more
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IEEE Access, 2016 Active learning aims at obtaining high-accuracy models with as a few labeled data as possible, by iteratively and elaborately selecting most valuable data to query labels during the learning process, thereby the cost of labeling data can be reduced. Most previous active learning approaches consider the situation of centralized processing, where all the
Pengcheng Shen +2 more
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Distributed Learning With Dependent Samples
IEEE Transactions on Information Theory, 2022 This paper focuses on learning rate analysis of distributed kernel ridge regression for strong mixing sequences. Using a recently developed integral operator approach and a classical covariance inequality for Banach-valued strong mixing sequences, we succeed in deriving optimal learning rate for distributed kernel ridge regression.
Zirui Sun, Shao-Bo Lin
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Overview of Distributed Machine Learning Techniques for 6G Networks
Algorithms, 2022 The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless ...
Eugenio Muscinelli +2 more
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Mobility-Aware Federated Learning Considering Multiple Networks
Sensors, 2023 Federated learning (FL) is a distributed training method for machine learning models (ML) that maintain data ownership on users. However, this distributed training approach can lead to variations in efficiency due to user behaviors or characteristics ...
Daniel Macedo +3 more
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