Abstract This paper explores two new weightless neural network models, Regression WiSARD and ClusRegression WiSARD, in the challenging task of predicting the total palm oil production of a set of 28 (twenty eight) differently located sites under different climate and soil profiles. Both models were derived from Kolcz and Allinson’s n-Tuple Regression weightless neural model and obtained mean absolute error (MAE) rates of 0.09097 and 0.09173, respectively. Such results are very competitive with the state-of-the-art (0.07983), whilst being four orders of magnitude faster during the training phase. Additionally the models have been tested on three classic regression datasets, also presenting competitive performance with respect to other models often used in this type of task.

中文翻译：

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扩展用于回归的 Weightless WiSARD 分类器

摘要 本文探讨了两种新的失重神经网络模型 Regression WiSARD 和 ClusRegression WiSARD，在预测不同气候和土壤剖面下一组 28 个（二十八个）不同位置的棕榈油总产量的挑战性任务中。两种模型均源自 Kolcz 和 Allinson 的 n-元组回归失重神经模型，获得的平均绝对误差 (MAE) 率分别为 0.09097 和 0.09173。这样的结果与最先进的 (0.07983) 非常有竞争力，同时在训练阶段要快四个数量级。此外，这些模型已经在三个经典的回归数据集上进行了测试，与此类任务中常用的其他模型相比，这些模型也表现出具有竞争力的性能。