• Title Support Vector Machines Succinctly
• Author(s) Alexandre Kowalczyk
• Publisher: Syncfusion Inc. (October 23, 2017)
• Paperback N/A
• ebook HTML, PDF (114 pages, 4.59 MB), ePub, Kindle (mobi)
• Language: English
• ISBN-10: N/A
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Book Description

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.

Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier.

Support Vector Machines (SVMs) are some of the most performant off-the-shelf, supervised machine-learning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problem-solving algorithms. He also includes numerous code examples and a lengthy bibliography for further study. By the end of the book, SVMs should be an important tool in the reader's machine-learning toolbox.

1. Prerequisites
2. The Perceptron
3. The SVM Optimization Problem
4. Solving the Optimization Problem
5. Soft Margin SVM
6. Kernels
7. The SMO Algorithm
8. Multi-Class SVMs
9. Conclusion
10. Appendix A: Datasets
11. Appendix B: The SMO Algorithm

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• Machine Learning
• Artificial Intelligence
• Neural Networks
• Operations Research (OR), Linear Programming, Optimization, and Approximation
• Algorithms and Data Structures
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