An incremental model selection algorithm based on cross-validation for finding the architecture of a Hidden Markov model on hand gesture data sets

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dc.authorid0000-0003-2225-7491
dc.authorid0000-0001-5838-4615
dc.contributor.authorUlaş, Aydınen_US
dc.contributor.authorYıldız, Olcay Taneren_US
dc.date.accessioned2019-07-31T13:40:08Z
dc.date.available2019-07-31T13:40:08Z
dc.date.issued2009-12-13
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn a multi-parameter learning problem, besides choosing the architecture of the learner, there is the problem of finding the optimal parameters to get maximum performance. When the number of parameters to be tuned increases, it becomes infeasible to try all the parameter sets, hence we need an automatic mechanism to find the optimum parameter setting using computationally feasible algorithms. In this paper, we define the problem of optimizing the architecture of a Hidden Markov Model (HMM) as a state space search and propose the MSUMO (Model Selection Using Multiple Operators) framework that incrementally modifies the structure and checks for improvement using cross-validation. There are five variants that use forward/backward search, single/multiple operators, and depth-first/breadth-first search. On four hand gesture data sets, we compare the performance of MSUMO with the optimal parameter set found by exhaustive search in terms of expected error and computational complexity.en_US
dc.description.sponsorshipCal State Universityen_US
dc.description.sponsorshipAssociation for Machine Learning and Applicationsen_US
dc.description.sponsorshipUniversity of Louisvilleen_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationUlaş, A. & Yıldız, O. T. (2009). An incremental model selection algorithm based on cross-validation for finding the architecture of a hidden markov model on hand gesture data sets. Paper presented at the 8th International Conference on Machine Learning and Applications, 170-177. doi:10.1109/ICMLA.2009.91en_US
dc.identifier.doi10.1109/ICMLA.2009.91
dc.identifier.endpage177
dc.identifier.isbn9780769539263
dc.identifier.scopus2-s2.0-77950895047
dc.identifier.scopusqualityN/A
dc.identifier.startpage170
dc.identifier.urihttps://hdl.handle.net/11729/1668
dc.identifier.urihttp://dx.doi.org/10.1109/ICMLA.2009.91
dc.identifier.wosWOS:000291011600024
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorYıldız, Olcay Taneren_US
dc.institutionauthorid0000-0001-5838-4615
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherIEEEen_US
dc.relation.ispartof8th International Conference on Machine Learning and Applicationsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHidden Markov modelen_US
dc.subjectModel selectionen_US
dc.subjectCross-validationen_US
dc.subjectAutomatic mechanismsen_US
dc.subjectData setsen_US
dc.subjectExhaustive searchen_US
dc.subjectFeasible algorithmsen_US
dc.subjectHand gestureen_US
dc.subjectIncremental modelsen_US
dc.subjectMultiparametersen_US
dc.subjectMultiple operatoren_US
dc.subjectOptimal parameteren_US
dc.subjectOptimum parametersen_US
dc.subjectParameter seten_US
dc.subjectState space searchen_US
dc.subjectComputational complexityen_US
dc.subjectMathematical operatorsen_US
dc.subjectOptimizationen_US
dc.subjectLearning systemsen_US
dc.subjectHidden Markov modelsen_US
dc.subjectMachine learningen_US
dc.subjectComputer architectureen_US
dc.subjectMachine learning algorithmsen_US
dc.subjectData engineeringen_US
dc.subjectGraphical modelsen_US
dc.subjectBayesian methodsen_US
dc.subjectApplication softwareen_US
dc.subjectState-space methodsen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectTree searchingen_US
dc.subjectIncremental model selection algorithmen_US
dc.subjectHand gesture data setsen_US
dc.subjectMultiparameter learning problemen_US
dc.subjectState space searchen_US
dc.subjectForward/backward searchen_US
dc.subjectSingle/multiple operatorsen_US
dc.subjectDepth-first/breadth-first searchen_US
dc.titleAn incremental model selection algorithm based on cross-validation for finding the architecture of a Hidden Markov model on hand gesture data setsen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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