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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
- ISBN-10: N/A
- ISBN-13: N/A
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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.
- Prerequisites
- The Perceptron
- The SVM Optimization Problem
- Solving the Optimization Problem
- Soft Margin SVM
- Kernels
- The SMO Algorithm
- Multi-Class 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
