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Support Vector Machines Succinctly

Published on: October 23, 2017
Support Vector Machines Succinctly

Author : Alexandre Kowalczyk

Categories :

SVMs, Bioinformatics, Data Classification, SVM Optimization

Pages : 114

Formats : Amazon Kindle and PDF

Price : FREE


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.

Table of Contents
  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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