Complete modified stacked denoising auto-encoder (CMSDAE) machines constitute a version of stacked auto-encoders in which a target estimate is included at the input, and are trained layer-by-layer by minimizing a convex combination of the errors corresponding to the input sample and the target. This permits to carry out the transformation of the observation space without forgetting what the target is. It has been shown in recent publications that this method produces a clear performance advantage in classification tasks. The above facts motivate to explore whether CMSDAE machines also offer performance improvements in regression problems, and in particular for time series prediction where conventional discriminative machines find difficulties: The layer-by-layer reconstruction of the target (together with the input) can help to reduce these difficulties. This contribution presents the CMSDAE regression/prediction machines and their design, showing experimental evidence of their frequent superior performance —never lower— with respect to other machine architectures. Some subsequent research directions are indicated together with the conclusions.

完整的改进的堆叠式去噪自动编码器（CMSDAE）机器构成了堆叠式自动编码器的一种版本，其中在输入处包含目标估计值，并通过最小化与输入相对应的误差的凸组合来逐层训练样本和目标。这允许执行观察空间的转换而不会忘记目标是什么。在最近的出版物中已经表明，该方法在分类任务中具有明显的性能优势。上述事实促使人们探索CMSDAE机器是否还可以改善回归问题的性能，尤其是对于传统判别机器遇到困难的时间序列预测：目标（与输入一起）的逐层重建可以帮助减少这些困难。此文稿介绍了CMSDAE回归/预测机器及其设计，显示了相对于其他机器体系结构而言，其频繁出众的性能（从未降低）的实验证据。指出了一些后续的研究方向和结论。