论文标题
通过人工碳氢化合物网络进行监督学习：开源实施及其应用
Supervised learning with artificial hydrocarbon networks: an open source implementation and its applications
论文作者
论文摘要
人工碳氢化合物网络（AHN）是一种基于有机化合物的结构和内部化学机制的新型监督学习方法。作为任何其他尖端算法，面临两个挑战：编码和与其他技术联系的耗时。事实证明，大型开源平台是解决后一种挑战的替代解决方案。从这个意义上讲，本文旨在为实现AHN的R引入AHNR软件包。它提供了几个功能来创建，训练，测试和可视化AHN。它还包括常规功能，可以轻松与受过训练的模型进行交互。出于插图目的，它提供了有关AHN在工程中的应用以及使用它的方法的几个示例。该软件包旨在对对机器学习和数据建模感兴趣的科学家和应用研究人员非常有用。包装可用性在综合的R档案网络中。
Artificial hydrocarbon networks (AHN) is a novel supervised learning method inspired on the structure and the inner chemical mechanisms of organic compounds. As any other cutting-edge algorithm, there are two challenges to be faced: time-consuming for encoding and complications to connect with other technologies. Large and open source platforms have proved to be an alternative solution to the latter challenges. In that sense, this paper aims to introduce the ahnr package for R that implements AHN. It provides several functions to create, train, test and visualize AHN. It also includes conventional functions to easily interact with the trained models. For illustration purposes, it presents several examples about the applications of AHN in engineering, as well as, the way to use it. This package is intended to be very useful for scientists and applied researchers interested in machine learning and data modeling. Package availability is in the Comprehensive R Archive Network.
