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

Language

English


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