Restricted Boltzmann machines (RBMs) are components found in many deep learning algorithms. RBMs originally were designed for binary image data. Some advances in RBM algorithms have been made so that they may accept real‐valued inputs that are typical for analytical chemistry measurements. However, these algorithms are difficult to train and require fine‐tuning of the parameters. The RBM algorithm was modified to furnish the enhanced zippy RBM (EZRBM) that trains reliably and robustly with respect to the parameters. In addition, feature augmentation (ie, fusing the RBM linear inputs and nonlinear outputs) improves the classification rate while reducing the dependence of the RBM training parameters. Two different classifiers were used, the support vector classifier (SVC) and super partial least squares–discriminant analysis (sPLS‐DA), to evaluate the performance. Classifiers built from the EZRBM outputs performed better than those built from the continuous RBM and the Gaussian RBM (GRBM) outputs when validated using 100 bootstraps with two Latin partitions. Three datasets were used. The first was an overdetermined set of eight fatty acid concentrations for 572 olive oils from nine regions of Italy. The second was 75 UV spectra of 15 Cannabis extracts with 101 measurements made from 200 to 400 nm. The third set comprised 60 proton nuclear magnetic resonance (NMR) spectra of 12 tea extracts that had 1000 chemical shift measurements from 0.5 to 7.0 ppm. In every evaluation, the augmented EZRBM had better classification performance than the classifiers without the RBM. The classifiers built with EZRBM outperformed the other RBM algorithms except for a single instance. Recently, RBMs have been considered a transform into a dual feature space.

中文翻译：

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用于特征扩展和改进的分析数据分类的增强型 zippy 受限玻尔兹曼机

受限玻尔兹曼机 (RBM) 是许多深度学习算法中的组件。RBM 最初是为二进制图像数据设计的。RBM 算法已经取得了一些进展，因此它们可以接受分析化学测量中典型的实值输入。然而，这些算法难以训练并且需要对参数进行微调。对 RBM 算法进行了修改，以提供增强的 zippy RBM (EZRBM)，该算法在参数方面可靠且稳健地训练。此外，特征增强（即融合 RBM 线性输入和非线性输出）提高了分类率，同时降低了 RBM 训练参数的依赖性。使用了两种不同的分类器，支持向量分类器（SVC）和超偏最小二乘判别分析（sPLS-DA），来评估性能。当使用具有两个拉丁分区的 100 个引导程序进行验证时，从 EZRBM 输出构建的分类器比从连续 RBM 和高斯 RBM (GRBM) 输出构建的分类器表现更好。使用了三个数据集。第一个是来自意大利九个地区的 572 种橄榄油的一组超定的八种​​脂肪酸浓度。第二个是 15 种大麻提取物的 75 个 UV 光谱，在 200 到 400 nm 范围内进行了 101 次测量。第三组包括 12 种茶提取物的 60 个质子核磁共振 (NMR) 光谱，具有 0.5 至 7.0 ppm 的 1000 次化学位移测量值。在每次评估中，增强的 EZRBM 都比没有 RBM 的分类器具有更好的分类性能。除了单个实例外，使用 EZRBM 构建的分类器优于其他 RBM 算法。最近，