发表机构
Indian Institute of Technology Indore（印度理工学院印多尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对随机神经网络单次隐藏单元抽取的局限，提出残差引导的贪婪隐藏层构建方法，在71个UCI基准数据集上验证其性能优于基线模型。
AI 中文摘要
随机神经网络通过将输入到隐藏层的参数随机固定，并以闭式形式学习输出权重，实现了快速且可解析处理的训练；然而，其性能关键取决于对隐藏单元的单次无信息抽取。这种单次且对任务无信息的特征构建常导致冗余表示和模型容量的次优利用。为解决此局限，我们提出一种简单且广泛适用的残差引导过程，该过程使用闭式残差减少准则贪婪地构建隐藏层。在每个阶段，我们（i）生成一组随机候选单元；（ii）根据每个候选单元在脊回归正则化目标中引发的精确减少量对其评分；（iii）选择前k个单元；（iv）使用带直接输入连接的标准设计以闭式形式重新拟合读出层。此过程产生渐进式训练过程，且保证训练目标单调递减。该方法与模型无关：仅候选生成与架构相关，而评分、选择、重新拟合循环在所有模型间共享。在来自UCI仓库的71个基准数据集（涵盖二分类和多分类任务）上进行的大量实验表明，所提出的残差引导模型在准确率、稳定性和整体排名性能方面始终优于其基线对应模型。
英文摘要
Randomized neural networks enable fast and analytically tractable training by fixing the input to hidden layer parameters at random and learning the output weights in closed form; however, their performance critically depends on a single uninformed draw of hidden units. This one shot and task uninformed feature construction often leads to redundant representations and suboptimal utilization of model capacity. To address this limitation, we propose a simple and broadly applicable residual guided procedure that greedily constructs the hidden layer using a closed form residual decrease criterion. At each stage, we (i) generate a pool of random candidate units, (ii) score each candidate by the exact reduction it induces in the ridge regularized objective, (iii) select the top k units, and (iv) refit the readout in closed form using the standard design with direct input links. This procedure yields a progressive training process with a guaranteed monotonic decrease of the training objective. The method is model agnostic: only the candidate generation is architecture specific, while the scoring selection refitting loop is shared across models. Extensive experiments on 71 benchmark datasets from the UCI repository, covering both binary and multiclass classification tasks, demonstrate that the proposed residual-guided models consistently outperform their baseline counterparts in terms of accuracy, stability, and overall ranking performance.
CommentsAccepted at WCCI 2026
