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Selective ensemble-based online adaptive deep neural networks for streaming data with concept drift
Abstract Concept drift is an important issue in the field of streaming data mining. However,
how to maintain real-time model convergence in a dynamic environment is an important and …
how to maintain real-time model convergence in a dynamic environment is an important and …
Online multi-agent forecasting with interpretable collaborative graph neural networks
This article considers predicting future statuses of multiple agents in an online fashion by
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …
exploiting dynamic interactions in the system. We propose a novel collaborative prediction …
On the Necessity of Collaboration for Online Model Selection with Decentralized Data
We consider online model selection with decentralized data over $ M $ clients, and study the
necessity of collaboration among clients. Previous work proposed various federated …
necessity of collaboration among clients. Previous work proposed various federated …
Concept drift adaptation with continuous kernel learning
Y Chen, HL Dai - Information Sciences, 2024 - Elsevier
Abstract Concept drift poses significant challenges in the fields of machine learning and data
mining. At present, many existing algorithms struggle to maintain low error rates or require …
mining. At present, many existing algorithms struggle to maintain low error rates or require …
Communication-efficient randomized algorithm for multi-kernel online federated learning
Online federated learning (OFL) is a promising framework to learn a sequence of global
functions from distributed sequential data at local devices. In this framework, we first …
functions from distributed sequential data at local devices. In this framework, we first …
Cost-sensitive online adaptive kernel learning for large-scale imbalanced classification
Y Chen, Z Hong, X Yang - IEEE Transactions on Knowledge …, 2023 - ieeexplore.ieee.org
Imbalanced classification is a challenging task in the fields of machine learning, data mining
and pattern recognition. Cost-sensitive online algorithms are very important methods for …
and pattern recognition. Cost-sensitive online algorithms are very important methods for …
Incremental ensemble Gaussian processes
Belonging to the family of Bayesian nonparametrics, Gaussian process (GP) based
approaches have well-documented merits not only in learning over a rich class of nonlinear …
approaches have well-documented merits not only in learning over a rich class of nonlinear …
Ensemble Gaussian processes with spectral features for online interactive learning with scalability
Combining benefits of kernels with Bayesian models, Gaussian process (GP) based
approaches have well-documented merits not only in learning over a rich class of nonlinear …
approaches have well-documented merits not only in learning over a rich class of nonlinear …
Nonlinear structural vector autoregressive models with application to directed brain networks
Structural equation models (SEMs) and vector autoregressive models (VARMs) are two
broad families of approaches that have been shown useful in effective brain connectivity …
broad families of approaches that have been shown useful in effective brain connectivity …
Personalized online federated learning with multiple kernels
Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear
function approximation. Federated learning enables a group of learners (called clients) to …
function approximation. Federated learning enables a group of learners (called clients) to …