Morse Code Symbol Classification

We present two synthetic datasets on classification of Morse code symbols for supervised machine learning problems, in particular, neural networks. The linked Github page has algorithms for generating a family of such datasets of varying difficulty. The datasets are spatially one-dimensional and have a small number of input features, leading to high density of input information content. This makes them particularly challenging when implementing network complexity reduction methods. The linked research paper explores the effects on network performance by deliberately adding various forms of noise and expanding the feature set and dataset size.

本报告呈现了两套针对莫尔斯电码符号分类任务的合成数据集，旨在解决监督机器学习问题，特别是神经网络的应用。相关Github页面提供了生成此类不同难度数据集的算法。该数据集在空间维度上为一维，且输入特征数量有限，从而导致了输入信息内容的密集度较高。这使得在实施网络复杂度降低方法时尤为具有挑战性。相关研究论文通过有意添加各种形式的噪声，并扩展特征集及数据集规模，探讨了这些措施对网络性能的影响。