The training procedure of the minimal learning machine (MLM) requires the selection of two sets of patterns from the training dataset. These sets are called input reference points (IRP) and output reference points (ORP), which are used to build a mapping between the input geometric configurations and their corresponding outputs. In the original MLM, the number of input reference points is the hyper-parameter and the patterns are chosen at random. Therefore, the conventional proposal does not consider which patterns will belong to each reference point group, since the model does not implement an appropriate way of selecting the most suitable patterns as reference points. Such an approach can impact on the decision function in terms of smoothness, resulting in high complexity models. This paper introduces a new approach to select IRP for MLM applied to classification tasks. The optimally selected minimal learning machine (OS-MLM) relies on the multiresponse sparse regression (MRSR) ranking method and the leave-one-out (LOO) criterion to sort the patterns in terms of relevance and select an appropriate number of input reference points, respectively. The experimental assessment conducted on UCI datasets reports the proposal was able to produce sparser models and achieve competitive performance when compared to the regular strategy of selecting MLM input RPs.

中文翻译：

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基于多响应稀疏回归的简约最小学习机

最小学习机 (MLM) 的训练过程需要从训练数据集中选择两组模式。这些集合称为输入参考点 (IRP) 和输出参考点 (ORP)，用于构建输入几何配置与其相应输出之间的映射。在原始 MLM 中，输入参考点的数量是超参数，模式是随机选择的。因此，传统的提议没有考虑哪些模式将属于每个参考点组，因为该模型没有实现选择最合适的模式作为参考点的适当方式。这种方法会在平滑度方面影响决策函数，从而导致模型复杂度高。本文介绍了一种新的方法来为 MLM 选择 IRP 应用于分类任务。最优选择的最小学习机 (OS-MLM) 依赖于多响应稀疏回归 (MRSR) 排序方法和留一法 (LOO) 标准根据相关性对模式进行排序并选择适当数量的输入参考点， 分别。在 UCI 数据集上进行的实验评估报告说，与选择 MLM 输入 RP 的常规策略相比，该提案能够生成更稀疏的模型并实现具有竞争力的性能。最优选择的最小学习机 (OS-MLM) 依赖于多响应稀疏回归 (MRSR) 排序方法和留一法 (LOO) 标准根据相关性对模式进行排序并选择适当数量的输入参考点， 分别。在 UCI 数据集上进行的实验评估报告说，与选择 MLM 输入 RP 的常规策略相比，该提案能够生成更稀疏的模型并实现具有竞争力的性能。最优选择的最小学习机 (OS-MLM) 依赖于多响应稀疏回归 (MRSR) 排序方法和留一法 (LOO) 标准根据相关性对模式进行排序并选择适当数量的输入参考点， 分别。在 UCI 数据集上进行的实验评估报告说，与选择 MLM 输入 RP 的常规策略相比，该提案能够生成更稀疏的模型并实现具有竞争力的性能。