Problem formulations and solvers in linear SVM: a review

Support vector machine (SVM) is an optimal margin based classification technique in machine learning. SVM is a binary linear classifier which has been extended to non-linear data using Kernels and multi-class data using various techniques like one-versus-one, one-versus-rest, Crammer Singer SVM, Wes...

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Vydáno v:The Artificial intelligence review Ročník 52; číslo 2; s. 803 - 855
Hlavní autoři: Chauhan, Vinod Kumar, Dahiya, Kalpana, Sharma, Anuj
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.08.2019
Springer
Springer Nature B.V
Témata:
Artificial Intelligence
Big Data
Classification
Classifiers
Computer Science
Data management
Formulations
Kernel functions
Machine learning
Solvers
Support vector machines
Word sense disambiguation
Support vector machines
Support vector classification
Linear SVM
SVM solvers
Optimization problem
ISSN:0269-2821, 1573-7462
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Shrnutí:Support vector machine (SVM) is an optimal margin based classification technique in machine learning. SVM is a binary linear classifier which has been extended to non-linear data using Kernels and multi-class data using various techniques like one-versus-one, one-versus-rest, Crammer Singer SVM, Weston Watkins SVM and directed acyclic graph SVM (DAGSVM) etc. SVM with a linear Kernel is called linear SVM and one with a non-linear Kernel is called non-linear SVM. Linear SVM is an efficient technique for high dimensional data applications like document classification, word-sense disambiguation, drug design etc. because under such data applications, test accuracy of linear SVM is closer to non-linear SVM while its training is much faster than non-linear SVM. SVM is continuously evolving since its inception and researchers have proposed many problem formulations, solvers and strategies for solving SVM. Moreover, due to advancements in the technology, data has taken the form of ‘Big Data’ which have posed a challenge for Machine Learning to train a classifier on this large-scale data. In this paper, we have presented a review on evolution of linear support vector machine classification, its solvers, strategies to improve solvers, experimental results, current challenges and research directions.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-018-9614-6