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.
Topics included: Prerequisites • The Perceptron • The SVM Optimization Problem • Solving the Optimization Problem • Soft Margin SVM • Kernels • The SMO Algorithm • Multi-Class SVMs • Conclusion • Datasets • The SMO Algorithm.
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Publisher: Syncfusion Inc.
Published: November 2017
Format(s): PDF, Mobi(Kindle)
File size: 3.20 MB
Number of pages: 114
Download / View Link(s): PDF, Mobi