Book (Practical) Information science Easy Learning Support Vector Machine

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Information Science
You can learn the basics and basics of the theory and practice of Support Vector Machine in this one book! Support Vector Machine is known as a powerful and flexible model for various machine learning situations such as data classification, regression, and outlier detection. The algorithm is intuitive and does not have mathematical ambiguity, so it can be said to be a powerful method in light of the evaluation criterion of "interpretability of machine learning" which is attracting attention recently. Therefore, it is easy to apply research results from natural science and economics, etc. and empirical business knowledge to the model. This book gently develops the theoretical framework of Support Vector Machine from high school level mathematics, and introduces easy-to-understand implementation examples in Python. In addition, the kernel method used for nonlinear Support Vector Machine, which is important for applications, is mathematically advanced, but it explains mathematics in an easy-to-understand way through illustrations and concrete numerical examples.