Artificial neural networks (ANNs) require tremendous amount of data to train on. However, in classification models, most data features are often similar which can lead to increase in training time without significant improvement in the performance. Thus, we hypothesize that there could be a more efficient way to train an ANN using a better representative sample. For this, we propose the LAD Improved Iterative Training (LIIT), a novel training approach for ANN using large deviations principle to generate and iteratively update training samples in a fast and efficient setting. This is exploratory work with extensive opportunities for future work. The thesis presents this ongoing research work with the following contributions from this study: (1) We propose a novel ANN training method, LIIT, based on the large deviations theory where additional dimensionality reduction is not needed to study high dimensional data. (2) The LIIT approach uses a Modified Training Sample (MTS) that is generated and iteratively updated using a LAD anomaly score based sampling strategy. (3) The MTS sample is designed to be well representative of the training data by including most anomalous of the observations in each class. This ensures distinct patterns and features are learnt with smaller samples. (4) We study the classification performance of the LIIT trained ANNs with traditional batch trained counterparts.

翻译：人工神经网络（ANN）需要大量数据进行训练。然而，在分类模型中，大多数数据特征往往相似，这可能导致训练时间增加而性能无明显提升。因此，我们假设存在一种更高效的方式，即通过更具代表性的样本训练ANN。为此，我们提出大偏差改进迭代训练（LIIT）——一种基于大偏差原理的ANN新颖训练方法，能够快速高效地生成并迭代更新训练样本。这是一项探索性研究，为未来工作提供了广泛空间。本文展示了这项正在进行的研究，主要贡献包括：(1) 提出基于大偏差理论的新型ANN训练方法LIIT，无需额外降维即可研究高维数据；(2) LIIT方法使用基于大偏差异常评分采样策略生成并迭代更新的修正训练样本（MTS）；(3) MTS样本通过包含每类中最异常的观测值，确保其能良好代表训练数据，从而在较小样本中学习到独特模式与特征；(4) 我们比较了LIIT训练ANN与传统批量训练ANN的分类性能。