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.

本文介绍了一个基于大偏差原理的新型人工神经网络训练方法 LAD Improved Iterative Training，该方法使用 Modified Training Sample（MTS）作为训练样本，采用异常得分为基础的采样策略，能更加有效地学习高维数据特征和模式，与传统批处理方式相比分类性能更优。