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September 5, 2025IEEE Transactions on Neural Networks and Learning Systems

Efficient High-Dimensional Learning With Adaptive Gaussian RBF Networks

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Authors

XGXiaoyu GaoXXXuetao XieJWJian Wang

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Overview

The study proposes a novel Gaussian kernel and a multioutput algorithm for efficient learning in high-dimensional data, addressing key limitations.

Key Points

  • The joint residual MOCD algorithm improves weight estimation, enhancing performance in high-dimensional settings.
  • Extensive experiments show that the dimensionality-adaptive Gaussian kernel effectively addresses activation issues.
  • The proposed methods demonstrate superior learning efficiency compared to traditional RBFNNs in complex data environments.
  • Parallel computation capabilities of the MOCD algorithm optimize processing, mitigating the burden of high-dimensional feature matrices.

Cite This Study

Gao et al. (2025) studied this question.

synapsesocial.com/papers/68bb3ee82b87ece8dc957285https://doi.org/10.1109/tnnls.2025.3601366
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