Publication:
Kernel methods and support vector machines for handwriting recognition

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dc.citedby6
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorKhalid M.en_US
dc.contributor.authorYusof R.en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid7101640051en_US
dc.contributor.authorid6603877546en_US
dc.date.accessioned2023-12-28T08:57:59Z
dc.date.available2023-12-28T08:57:59Z
dc.date.issued2002
dc.description.abstractThis 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo1033120
dc.identifier.doi10.1109/SCORED.2002.1033120
dc.identifier.epage312
dc.identifier.scopus2-s2.0-84971656962
dc.identifier.spage309
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84971656962&doi=10.1109%2fSCORED.2002.1033120&partnerID=40&md5=2234c3364f93d96ea3dccd7aafe26899
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/29864
dc.pagecount3
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2002 Student Conference on Research and Development: Globalizing Research and Development in Electrical and Electronics Engineering, SCOReD 2002 - Proceedings
dc.subjectArtificial intelligence
dc.subjectCharacter recognition
dc.subjectClassification (of information)
dc.subjectGeometry
dc.subjectLearning systems
dc.subjectQuadratic programming
dc.subjectClassification tasks
dc.subjectHandwriting recognition
dc.subjectHigher dimensional features
dc.subjectKernel function
dc.subjectKernel methods
dc.subjectMaximal margin
dc.subjectSequential minimization optimizations
dc.subjectTraining data
dc.subjectSupport vector machines
dc.titleKernel methods and support vector machines for handwriting recognitionen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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