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About 10,000 papers for “Support vector machine”

Support Vector, Support Vector Machine

2008

Abstract- Neural network learning method called Support Vector Neural Trainig was outlined in year 2004 by W. Duch. Goal of this method is to select only those vectors from training set, wich influence at learning process is the biggest. Even though, that this access work well, is possible to make different modifications, so that on a certain data type was the reached accuracy higher or the time necessary to network learning was shorter. This work handle currently these modifications. Project definition Primary targets of this work: • Work out review of Support Vector Machine techniques in the Neural Network trainig • Design optimal combination of learning algorithms in the NN and SVM • Implementation of selected algorithms • Realize comparative experiments on implemented algorithms • Evaluate realized experiments and algorithms

Cited by 0

Support Vector Machine Solvers Support Vector Machine Solvers

Léon Bottou, Chih-jen Lin · 2008

Considerable efforts have been devoted to the implementation of efficient optimization method for solving the Support Vector Machine dual problem. This document proposes an historical perspective and and in depth review of the algorithmic and computational issues associated with this problem. 1.

Cited by 0

Support Vector Machine

Deepti Chopra, Roopal Khurana · BENTHAM SCIENCE PUBLISHERS eBooks · 2023

Support Vector Machine (SVM) may be defined as a machine learning algorithm that can be used for regression and classification. It is generally used for classification purposes. In this chapter, we will discuss Margin and Large Margin Methods and Kernel Methods. <br>

Cited by 1

Support vector machine.

Sujin Pyo (4591705), Jaewook Lee (329325), Mincheol Cha (4591702) et al. · Figshare · 2017

<p>Given data set of <i>n</i> points (<b>x</b><sub><b>1</b></sub>, <i>y</i><sub>1</sub>), (<b>x</b><sub><b>2</b></sub>, <i>y</i><sub>2</sub>), …, (<b>x</b><sub><b>n</b></sub>, <i>y</i><sub><i>n</i></sub>) where <i>y</i><sub><i>i</i></sub> gives -1 or 1 which indicates the class and <b>x</b><sub><b>i</b></sub> is a <i>p</i>-dimensional vector, we want to classify the points by finding “maximum-margin hyperplane” which divides the data into two groups with at the highest probability. This means that the distance between the hyperplane and the points which are the closest to the hyperplane in each class should be maximized.</p>

Cited by 0Open access

Support Vector Machine

Ruye Wang · Cambridge University Press eBooks · 2025

Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.

Cited by 0

Support vector machine

Ran Yan, Shuaian Wang · 2022

This chapter first introduces one of the most popular machine learning models for classification tasks called support vector machine (SVM). Then, kernel trick to improve its prediction accuracy while reducing the computation burden is discussed. The extension of SVM to address regression tasks, which is called support vector regression, is then presented.

Cited by 9

Support Vector Machine

A. B. M. Shawkat Ali · IGI Global eBooks · 2009

From the beginning, machine learning methodology, which is the origin of artificial intelligence, has been rapidly spreading in the different research communities with successful outcomes. This chapter aims to introduce for system analysers and designers a comparatively new statistical supervised machine learning algorithm called support vector machine (SVM). We explain two useful areas of SVM, that is, classification and regression, with basic mathematical formulation and simple demonstration to make easy the understanding of SVM. Prospects and challenges of future research in this emerging area are also described. Future research of SVM will provide improved and quality access to the users. Therefore, developing an automated SVM system with state-of-the-art technologies is of paramount importance, and hence, this chapter will link up an important step in the system analysis and design perspective to this evolving research arena.

Cited by 10