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Kernel methods and support vector machines for handwriting recognition

This paper presents a review of kernel methods in machine learning. The support vector machine (SVM) as one of the methods in machine learning to make use of kernels is first discussed with the intention of applying it to handwriting recognition. SVM works by mapping training data for a classificati...

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Main Authors:	Ahmad A.R., Khalid M., Yusof R.
Other Authors:	35589598800
Format:	Conference paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
Subjects:	Artificial intelligence Character recognition Classification (of information) Geometry Learning systems Quadratic programming Classification tasks Handwriting recognition Higher dimensional features Kernel function Kernel methods Maximal margin Sequential minimization optimizations Training data Support vector machines
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id	my.uniten.dspace-29864
record_format	dspace
spelling	my.uniten.dspace-298642023-12-28T16:57:59Z Kernel methods and support vector machines for handwriting recognition Ahmad A.R. Khalid M. Yusof R. 35589598800 7101640051 6603877546 Artificial intelligence Character recognition Classification (of information) Geometry Learning systems Quadratic programming Classification tasks Handwriting recognition Higher dimensional features Kernel function Kernel methods Maximal margin Sequential minimization optimizations Training data Support vector machines This paper presents a review of kernel methods in machine learning. The support vector machine (SVM) as one of the methods in machine learning to make use of kernels is first discussed with the intention of applying it to handwriting recognition. SVM works by mapping training data for a classification task into a higher dimensional feature space using the kernel function and then finding a maximal margin hyperplane, which separates the mapped data. Finding the solution hyperplane involves using quadratic programming which is computationally intensive. Algorithms for practical implementation such as sequential minimization optimization (SMO) and its improvements are discussed. A few simpler methods similar to SVM but requiring simpler computation are also mentioned for comparison. Usage of SVM for handwriting recognition is then proposed. � 2002 IEEE. Final 2023-12-28T08:57:59Z 2023-12-28T08:57:59Z 2002 Conference paper 10.1109/SCORED.2002.1033120 2-s2.0-84971656962 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971656962&doi=10.1109%2fSCORED.2002.1033120&partnerID=40&md5=2234c3364f93d96ea3dccd7aafe26899 https://irepository.uniten.edu.my/handle/123456789/29864 1033120 309 312 Institute of Electrical and Electronics Engineers Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Artificial intelligence Character recognition Classification (of information) Geometry Learning systems Quadratic programming Classification tasks Handwriting recognition Higher dimensional features Kernel function Kernel methods Maximal margin Sequential minimization optimizations Training data Support vector machines
spellingShingle	Artificial intelligence Character recognition Classification (of information) Geometry Learning systems Quadratic programming Classification tasks Handwriting recognition Higher dimensional features Kernel function Kernel methods Maximal margin Sequential minimization optimizations Training data Support vector machines Ahmad A.R. Khalid M. Yusof R. Kernel methods and support vector machines for handwriting recognition
description	This paper presents a review of kernel methods in machine learning. The support vector machine (SVM) as one of the methods in machine learning to make use of kernels is first discussed with the intention of applying it to handwriting recognition. SVM works by mapping training data for a classification task into a higher dimensional feature space using the kernel function and then finding a maximal margin hyperplane, which separates the mapped data. Finding the solution hyperplane involves using quadratic programming which is computationally intensive. Algorithms for practical implementation such as sequential minimization optimization (SMO) and its improvements are discussed. A few simpler methods similar to SVM but requiring simpler computation are also mentioned for comparison. Usage of SVM for handwriting recognition is then proposed. � 2002 IEEE.
author2	35589598800
author_facet	35589598800 Ahmad A.R. Khalid M. Yusof R.
format	Conference paper
author	Ahmad A.R. Khalid M. Yusof R.
author_sort	Ahmad A.R.
title	Kernel methods and support vector machines for handwriting recognition
title_short	Kernel methods and support vector machines for handwriting recognition
title_full	Kernel methods and support vector machines for handwriting recognition
title_fullStr	Kernel methods and support vector machines for handwriting recognition
title_full_unstemmed	Kernel methods and support vector machines for handwriting recognition
title_sort	kernel methods and support vector machines for handwriting recognition
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
_version_	1806426282633723904
score	13.222552

Kernel methods and support vector machines for handwriting recognition

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