Results 41 to 50 of about 5,420 (166)

Adaptive tensor train learning algorithm based on single-aspect streaming model

Tongxin xuebao, 2023
An adaptive tensor train (TT) learning algorithm for the online decomposition problem of high-order tensors in single-aspect streaming model was investigated.Firstly, it was deduced that single-aspect streaming increment only changes the dimension of ...
Baoze MA, Guojun LI, Long XING, Changrong YE   +3 more
doaj   +2 more sources

Predicting Coronavirus Pandemic in Real-Time Using Machine Learning and Big Data Streaming System

Complexity, 2020
Twitter is a virtual social network where people share their posts and opinions about the current situation, such as the coronavirus pandemic. It is considered the most significant streaming data source for machine learning research in terms of analysis,
Xiongwei Zhang, Hager Saleh, Eman M. G. Younis, Radhya Sahal, Abdelmgeid A. Ali   +4 more
doaj   +1 more source

Online and Streaming Algorithms for Constrained k-Submodular Maximization

Proceedings of the AAAI Conference on Artificial Intelligence
Constrained k-submodular maximization is a general framework that captures many discrete optimization problems such as ad allocation, influence maximization, personalized recommendation, and many others. In many of these applications, datasets are large or decisions need to be made in an online manner, which motivates the development of efficient ...
Spaeh, Fabian, Ene, Alina, Nguyen, Huy L.   +2 more
openaire   +2 more sources

Quantum versus Classical Online Streaming Algorithms with Advice

, 2018
We consider online algorithms with respect to the competitive ratio. Here, we investigate quantum and classical one-way automata with non-constant size of memory (streaming algorithms) as a model for online algorithms. We construct problems that can be solved by quantum online streaming algorithms better than by classical ones in a case of logarithmic ...
Khadiev, Kamil, Khadieva, Aliya, Ziatdinov, Mansur, Kravchenko, Dmitry, Rivosh, Alexander, Yamilov, Ramis, Mannapov, Ilnaz   +6 more
openaire   +2 more sources

Improving the Quality of Experience of Video Streaming Through a Buffer-Based Adaptive Bitrate Algorithm and Gated Recurrent Unit-Based Network Bandwidth Prediction

Applied Sciences
With the evolution of cellular networks and wireless-local-area-network-based communication technologies, services for smart device users have appeared.
Jeonghun Woo, Seungwoo Hong, Donghyun Kang, Donghyeok An   +3 more
doaj   +1 more source

One-pass-throw-away learning for cybersecurity in streaming non-stationary environments by dynamic stratum network. [PDF]

PLoS ONE, 2018
Throughout recent times, cybersecurity problems have occurred in various business applications. Although previous researchers proposed to cope with the occurrence of cybersecurity issues, their methods repeatedly replicated the training processes for ...
Mongkhon Thakong, Suphakant Phimoltares, Saichon Jaiyen, Chidchanok Lursinsap   +3 more
doaj   +1 more source

Understanding Privacy-Utility Tradeoffs in Differentially Private Online Active Learning

The Journal of Privacy and Confidentiality, 2020
We consider privacy-preserving learning in the context of online learning. Insettings where data instances arrive sequentially in streaming fashion, incremental trainingalgorithms such as stochastic gradient descent (SGD) can be used to learn and ...
Daniel M Bittner, Alejandro E Brito, Mohsen Ghassemi, Shantanu Rane, Anand D Sarwate, Rebecca N Wright   +5 more
doaj   +1 more source

An Online Incremental Adaptation Mechanism to Subdue the Effect of Drift in Streaming Data

ITEGAM-JETIA
Concept drift detection and adaptation is one of the crucial components of a resilient machine learning pipeline in production. The Adaboost is an ensemble approach that incorporates incremental learning, that is widely used for concept drift adaptation
Ushashree P, R B V Subramanyam
doaj   +1 more source

From Batch to Stream: Automatic Generation of Online Algorithms

Proceedings of the ACM on Programming Languages
Online streaming algorithms, tailored for continuous data processing, offer substantial benefits but are often more intricate to design than their offline counterparts. This paper introduces a novel approach for automatically synthesizing online streaming algorithms from their offline versions. In particular, we propose a novel methodology,
Ziteng Wang, Shankara Pailoor, Aaryan Prakash, Yuepeng Wang, Işıl Dillig   +4 more
openaire   +2 more sources

An ensemble-based online learning algorithm for streaming data

, 2017
In this study, we introduce an ensemble-based approach for online machine learning. The ensemble of base classifiers in our approach is obtained by learning Naive Bayes classifiers on different training sets which are generated by projecting the original training set to lower dimensional space.
Nguyen, Tien Thanh, Nguyen, Thi Thu Thuy, Pham, Xuan Cuong, Liew, Alan Wee-Chung, Bezdek, James C.   +4 more
openaire   +2 more sources