Support Vector Machine Examples With Matlab


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Support Vector Machine. Examples with MATLAB


Support Vector Machine. Examples with MATLAB

Author: J. Smith

language: en

Publisher: Createspace Independent Publishing Platform

Release Date: 2017-05-02


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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. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. This book develops Support Vector Machine techniques.

Support Vector Machines for Antenna Array Processing and Electromagnetics


Support Vector Machines for Antenna Array Processing and Electromagnetics

Author: Manel Martínez-Ramón

language: en

Publisher: Springer Nature

Release Date: 2022-06-01


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Support Vector Machines (SVM) were introduced in the early 90's as a novel nonlinear solution for classification and regression tasks. These techniques have been proved to have superior performances in a large variety of real world applications due to their generalization abilities and robustness against noise and interferences. This book introduces a set of novel techniques based on SVM that are applied to antenna array processing and electromagnetics. In particular, it introduces methods for linear and nonlinear beamforming and parameter design for arrays and electromagnetic applications.

Support Vector Machines and Their Application in Chemistry and Biotechnology


Support Vector Machines and Their Application in Chemistry and Biotechnology

Author: Yizeng Liang

language: en

Publisher: CRC Press

Release Date: 2016-04-19


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Support vector machines (SVMs) are used in a range of applications, including drug design, food quality control, metabolic fingerprint analysis, and microarray data-based cancer classification. While most mathematicians are well-versed in the distinctive features and empirical performance of SVMs, many chemists and biologists are not as familiar wi