Machine learning for big sequence data: Wavelet-compressed Hidden Markov Models

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dc.contributor.authorBello, Luca
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerSchliep, Alexander
dc.contributor.supervisorGarza, Paolo
dc.contributor.supervisorSchliep, Alexander
dc.date.accessioned2020-09-18T13:26:40Z
dc.date.available2020-09-18T13:26:40Z
dc.date.issued2020sv
dc.date.submitted2020
dc.description.abstractHidden Markov models are among the most important machine learning methods for the statistical analysis of sequential data, but they struggle when applied on big data. Their relative inefficiency has been addressed several times by the use of some compression techniques, either for the computation. This thesis explores the former, with the application of a data compression technique based on wavelets and the subsequent adaptation of the main HMMs algorithms from the literature: the forward, Viterbi and Baum-Welch algorithms used to solve the evaluation, decoding and training problem respectively. The testing phase shows that this new technique generally yields equal or better results, obtaining some extremely high speedups in the training problem, making it even thousands of times faster; this allows to easily train a HMM with big data on a commodity laptop.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/301733
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectmachinesv
dc.subjectlearningsv
dc.subjectsequencesv
dc.subjectwaveletsv
dc.subjectcompressionsv
dc.subjecthiddensv
dc.subjectmarkovsv
dc.subjectmodelssv
dc.subjectviterbisv
dc.subjecttrainingsv
dc.titleMachine learning for big sequence data: Wavelet-compressed Hidden Markov Modelssv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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