 Название: How to Fine-tune Support Vector Machines for Classification
Автор: Ionut Brandusoiu, Gavril Toderean
Издательство: GAER Publishing House
Год: 2020
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
In Machine Learning (ML), support-vector machines (SVMs) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. This book covers in the first part the theoretical aspects of support vector machines and their functionality, and then based on the discussed concepts it explains how to find-tune a support vector machine to yield highly accurate prediction results which are adaptable to any classification tasks. The introductory part is extremely beneficial to someone new to learning support vector machines, while the more advanced notions are useful for everyone who wants to understand the mathematics behind support vector machines and how to find-tune them in order to generate the best predictive performance of a certain classification model.
For a better understanding of the underlying theory of support vector machines for classification, Chapter 1 describes supervised learning in the context of Machine Learning and all its related concepts. In terms of the generalization error as the metric to evaluate the performance of a classification model, the second half of this chapter contrasts between theoretical and empirical estimations.
Chapter 2 starts with the introduction to support vector machines, followed by theoretical foundations pertaining to their attributes in supervised learning. It discusses the technique of mapping independent variables into a high-dimensional space and several noteworthy research papers existent in the literature regarding various training techniques.
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How to Fine-tune Support Vector Machines for Classification 
