HiER-BLS：一种层级引导与纠错的鲁棒增量式宽度学习系统

中文摘要 AI 辅助

宽度学习系统（BLS）支持解析式训练和增量式扩展，但其增长需要指导新块应学习哪些输入。权重误差通过将已学习的输出跨类别边界位移，构成了进一步的挑战。我们提出HiER-BLS，将层级引导的表示增长与纠错学习相结合。连续块聚焦于由特征重要性和相关性选择的输入，同时保留早期表示。演化分支通过子空间大小和样本置信度引导编码学习者，使其学习经验同时影响特征视图和监督信号。对于有限宽度读出，我们展示了码字相关性如何变换拟合的类别分数。预测保持取决于实际输出到最近解码边界的距离相对于模型对权重误差的敏感性。在五个图像和五个表格数据集上的实验表明，与代表性BLS变体相比，分类性能有所提升。组件研究表明，即使引导分支的独立准确率较低，层级引导也有益于编码分支，且结合两者的分数可带来进一步增益。在干净准确率基本饱和后，更长的码在更强的高斯权重误差下仍能持续提高准确率。

英文摘要

Broad Learning System (BLS) supports analytical training and incremental expansion, but its growth needs guidance on which inputs new blocks should learn from. Weight errors pose a further challenge by displacing learned outputs across class boundaries. We propose HiER-BLS to couple hierarchy-guided representation growth with error-correcting learning. Successive blocks focus on inputs selected by feature importance and correlation while preserving earlier representations. The evolving branch guides encoded learners through subspace size and sample confidence, so its learning experience informs both their feature views and supervision. For finite broad readouts, we show how codeword correlations transform fitted class scores. Prediction preservation depends on the distance from the actual output to the nearest decoding boundary relative to the model's sensitivity to weight errors. Experiments on five image and five tabular datasets demonstrate improved classification performance over representative BLS variants. Component studies show that hierarchy guidance benefits the encoded branch even when the guiding branch has lower standalone accuracy, with further gains from combining their scores. Longer codes continue to improve accuracy under stronger Gaussian weight errors after clean accuracy has largely saturated.

发表机构

• South China University of Technology（华南理工大学）
• Pazhou Lab（琶洲实验室）

机构由 AI 辅助整理，请以论文原文为准。