Master of Science in Computer Science
First Committee Member
Kazem Taghva, Chair
Second Committee Member
Ajoy K. Datta
Third Committee Member
Laxmi P Gewali
Graduate Faculty Representative
Number of Pages
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.
Digital filters (Mathematics); Hidden Markov models; Smoothing (Numerical analysis); Smoothing (Statistics); Smoothness of functions
Computer Sciences | Theory and Algorithms
Boodidhi, Sweatha, "Using smoothing techniques to improve the performance of Hidden Markov’s Model" (2011). UNLV Theses, Dissertations, Professional Papers, and Capstones. 1007.