Results 31 to 40 of about 229,000 (311)
Online learning over graphs [PDF]
, 2005 We apply classic online learning techniques similar to the perceptron algorithm to the problem of learning a function defined on a graph. The benefit of our approach includes simple algorithms and performance guarantees that we naturally interpret in ...
Mark Herbster +5 more
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Capped
IEEE Access, 2019 As one of the most popular clustering techniques, graph clustering has attracted many researchers in the field of machine learning and data mining. Generally speaking, graph clustering partitions the data points into different categories according to ...
Mulin Chen +3 more
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Robust Graph Neural Networks via Ensemble Learning
Mathematics, 2022 Graph neural networks (GNNs) have demonstrated a remarkable ability in the task of semi-supervised node classification. However, most existing GNNs suffer from the nonrobustness issues, which poses a great challenge for applying GNNs into sensitive ...
Qi Lin +6 more
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Irregularly Sampled Multivariate Time Series Classification: A Graph Learning Approach [PDF]
, 2023 To date, graph-based learning methods are proven to be effective for modeling spatial and structural dependencies. However, when applied to IS-MTS, they encounter three major challenges due to the complex data characteristics of IS-MTS: 1) variable time ...
Jiang, Ting +9 more
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Remote Sensing, 2022 Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task.
Shu Tian +9 more
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Graph Algorithm Animation with Grrr [PDF]
, 2000 We discuss geometric positioning, highlighting of visited nodes and user defined highlighting that form the algorithm animation facilities in the Grrr graph rewriting programming language. The main purpose of animation was initially for the debugging and
Peter J. Rodgers +3 more
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Dual Space Graph Contrastive Learning [PDF]
, 2022 Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain.
Li, L +5 more
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Learning Graph Quantization [PDF]
, 2010 This contribution extends learning vector quantization to the domain of graphs. For this, we first identify graphs with points in some orbifold, then derive a generalized differentiable intrinsic metric, and finally extend the update rule of LVQ for generalized differentiable distance metrics.
Brijnesh J. Jain +3 more
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Temporal Multiresolution Graph Learning
IEEE Access, 2021 Estimating time-varying graphs, i.e., a set of graphs in which one graph represents the relationship among nodes in a certain time slot, from observed data is a crucial problem in signal processing, machine learning, and data mining.
Koki Yamada, Yuichi Tanaka
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Online Graph Dictionary Learning [PDF]
, 2021 International audienceDictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements.
Flamary, Rémi +4 more
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