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 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
 ISBN10: N/A
 ISBN13: 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 nonprobabilistic binary linear classifier.
Support Vector Machines (SVMs) are some of the most performant offtheshelf, supervised machinelearning algorithms. In Support Vector Machines Succinctly, author Alexandre Kowalczyk guides readers through the building blocks of SVMs, from basic concepts to crucial problemsolving 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 machinelearning toolbox.
 Prerequisites
 The Perceptron
 The SVM Optimization Problem
 Solving the Optimization Problem
 Soft Margin SVM
 Kernels
 The SMO Algorithm
 MultiClass SVMs
 Conclusion
 Appendix A: Datasets
 Appendix B: The SMO Algorithm
 N/A
 Machine Learning
 Artificial Intelligence
 Neural Networks
 Operations Research (OR), Linear Programming, Optimization, and Approximation
 Algorithms and Data Structures
 Succinctly Free eBooks Series
 Support Vector Machines Succinctly (Alexandre Kowalczyk)
 The Mirror Site (1)  PDF
 Interview with the Author

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