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SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS

DESAI, PRANAY A
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500

Abstract Details

2005, MS, University of Cincinnati, Engineering : Computer Science.
The field of Bio-Informatics is fast growing with research in various related topics. One such topic is protein sequence classification. This thesis uses this topic as motivation to develop a methodology that uses Hidden Markov Models (HMMs) to classify sequences. Hidden Markov Models are a concept in probability theory widely known for their application in the speech recognition. The three phases of HMMs: training, decoding, and evaluation, are used to classify sequences into clusters that have known similar functional properties. The training phase of HMMs uses a cluster of sequences to learn a model that is most likely to generate the sequences in the training cluster. The decoding and evaluation phases of HMMs use the generated model to calculate the likelihood of an unknown sequence belonging to the same sequence and generating a most-probable path the sequence traverses. The thesis presents background on HMMs along with detailed explanations of the algorithms used to implement all three phases of HMMs. The primary focus of this thesis is on the training phase of HMMs. During the implementation of the training phase we discovered that the phase has a numerical and computational weakness relating to those structures in which some silent states are included as part of the model. The results presented in this thesis test the training algorithms, show their workability and weaknesses, and point towards the silent states related weakness.
Dr. Raj Bhatnagar (Advisor)
72 p.
HMM; Hidden Markov Models; Sequence Classification; Viterbi Algorithm; Forward Algorithm; Backward Algorithm

Recommended Citations

Citations

  • DESAI, P. A. (2005). SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500

    APA Style (7th edition)

  • DESAI, PRANAY. SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS. 2005. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500.

    MLA Style (8th edition)

  • DESAI, PRANAY. "SEQUENCE CLASSIFICATION USING HIDDEN MARKOV MODELS." Master's thesis, University of Cincinnati, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1116250500

    Chicago Manual of Style (17th edition)

ucin1116250500
944
© 2005, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.