Results 31 to 40 of about 5,816 (262)
An ensemble-based online learning algorithm for streaming data
CoRR, 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.
Tien Thanh Nguyen +4 more
openaire +2 more sources
Entropy, 2023 Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios.
Jinghua Liu +4 more
doaj +1 more source
Bayesian Penalized Method for Streaming Feature Selection
IEEE Access, 2019 The online feature selection with streaming features has become more and more important in recent years. In contrast to standard feature selection method, streaming feature selection method can select feature dynamically without exploring full feature ...
Xiao-Ting Wang, Xin-Ze Luan
doaj +1 more source
SATE: Providing Stable and Agile Adaptation in HTTP-Based Video Streaming
IEEE Access, 2019 Online video streaming service, such as Youtube and Netflix, is emerging as a killer application that constitutes most IP traffic. Users want high-quality video streaming service; however, network bandwidth cannot keep up user's demand. In the HTTP-based
Wangyu Choi, Jongwon Yoon
doaj +1 more source
Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices
Frontiers in Neuroscience, 2021 While promising for high-capacity machine learning accelerators, memristor devices have non-idealities that prevent software-equivalent accuracies when used for online training.
Junyun Zhao +5 more
doaj +1 more source
A Comparison of Trajectory Compression Algorithms Over AIS Data
IEEE Access, 2021 Today’s industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them
Antonios Makris +3 more
doaj +1 more source
Learning from time-dependent streaming data with online stochastic algorithms
Trans. Mach. Learn. Res., 2022 This paper addresses stochastic optimization in a streaming setting with time-dependent and biased gradient estimates. We analyze several first-order methods, including Stochastic Gradient Descent (SGD), mini-batch SGD, and time-varying mini-batch SGD, along with their Polyak-Ruppert averages.
Antoine Godichon-Baggioni +2 more
openaire +3 more sources
Advanced Materials, EarlyView.Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje +7 more
wiley +1 more source
Algorithms, 2020 We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and affect memory and computational savings by processing the data on-the-fly as they are acquired.
Rasoul Shafipour, Gonzalo Mateos
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Online and Streaming Algorithms for Constrained k-Submodular Maximization
Proceedings of the AAAI Conference on Artificial IntelligenceConstrained 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 ...
Fabian Christian Spaeh +2 more
openaire +2 more sources