Can machine learning algorithms be implemented using chemistry? We demonstrate that this is possible in the case of support vector machines (SVMs). SVMs are powerful tools for data classification, leveraging Vapnik-Chervonenkis theory to handle high-dimensional data and small datasets effectively. In this work, we propose a chemical reaction network scheme for implementing SVMs, utilizing the steady-state behavior of reaction network dynamics to model key computational aspects of SVMs. This approach introduces a novel biochemical framework for implementing machine learning algorithms in non-traditional computational environments.

翻译：能否利用化学反应来实现机器学习算法？我们证明，在支持向量机（SVM）的情形下这是可行的。支持向量机是数据分类的强大工具，它借助Vapnik-Chervonenkis理论，能够有效处理高维数据和小样本数据集。本文提出了一种用于实现支持向量机的化学反应网络方案，利用反应网络动力学的稳态行为来模拟支持向量机的关键计算环节。该方案为在非传统计算环境中实现机器学习算法提供了一种新颖的生物化学框架。