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Research questionHow can Broad Learning Systems resist extreme errors and unreliable samples during noisy training?Least-squares training can let a few large residuals dominate the model, while treating every sample equally can amplify unreliable or locally conflicting examples. The problem is to limit both forms of distortion without losing useful information from difficult samples.
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave LossThe source studies UCI benchmark datasets and additional corruption experiments for Broad Learning Systems. It evaluates an objective combining bounded asymmetric residual penalties with intuitionistic-fuzzy sample credibility and uses accelerated gradient optimization instead of explicit matrix inversion; the reported evidence is limited to those benchmark and corruption settings.research paper · Sep 2, 2026
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