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.

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