Award Date
5-2011
Degree Type
Thesis
Degree Name
Master of Science in Computer Science
Department
Computer Science
First Committee Member
Kazem Taghva, Chair
Second Committee Member
Ajoy K. Datta
Third Committee Member
Laxmi P Gewali
Graduate Faculty Representative
Venkatesan Muthukumar
Number of Pages
48
Abstract
The result of training a HMM using supervised training is estimated probabilities for emissions and transitions. There are two difficulties with this approach Firstly, sparse training data causes poor probability estimates. Secondly, unseen probabilities have emission probability of zero. In this thesis, we report on different smoothing techniques and their implementations. We further report on our experimental results using standard precision and recall for various smoothing techniques.
Keywords
Digital filters (Mathematics); Hidden Markov models; Smoothing (Numerical analysis); Smoothing (Statistics); Smoothness of functions
Disciplines
Computer Sciences | Theory and Algorithms
File Format
Degree Grantor
University of Nevada, Las Vegas
Language
English
Repository Citation
Boodidhi, Sweatha, "Using smoothing techniques to improve the performance of Hidden Markov’s Model" (2011). UNLV Theses, Dissertations, Professional Papers, and Capstones. 1007.
http://dx.doi.org/10.34917/2349611
Rights
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