In machine learning method, the number of training samples is an exceedingly important factor determining the learning system’s robustness. In our previous researches (Liu et al., J. Syst. Eng. Electron. 27.2:333–342, 2016; Liu et al., IET Commun. 11.7:1000–1007, 2017), the extreme learning machines (ELMs) have proven to be an effective and time-saving learning method for pattern classification and the signal modulation recognition. ELMs are utilized to supervised learning issues principally on signal modulation recognition. In this thesis, ELMs are extended for semi-supervised tasks that are based on the manifold regularization, therefore greatly enlarging ELMs’ applicability. This article evolves countermeasures to the less training samples which mitigate the modulation recognition efficacy and demonstrates the robustness of semi-supervised learning for signal classification in AWGN and Rayleigh-fading channels.

中文翻译：

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解决信号调制识别中学习样本少的新方法

在机器学习方法中，训练样本的数量是决定学习系统鲁棒性的一个极其重要的因素。在我们之前的研究中（Liu等人，J. Syst。Eng。Electron。27.2：333–342，2016; Liu等人，IET Commun。11.7：1000-1007，2017），极限学习机（ELM）已被证明是一种有效且省时的模式分类和信号调制识别学习方法。ELM主要用于监督信号调制识别方面的学习问题。本文将ELM扩展为基于流形正则化的半监督任务，从而大大提高了ELM的适用性。