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Full-Text Articles in Statistics and Probability
Automating Construction And Selection Of A Neural Network Using Stochastic Optimization, Jason Lee Hurt
Automating Construction And Selection Of A Neural Network Using Stochastic Optimization, Jason Lee Hurt
UNLV Theses, Dissertations, Professional Papers, and Capstones
An artificial neural network can be used to solve various statistical problems by approximating a function that provides a mapping from input to output data. No universal method exists for architecting an optimal neural network. Training one with a low error rate is often a manual process requiring the programmer to have specialized knowledge of the domain for the problem at hand.
A distributed architecture is proposed and implemented for generating a neural network capable of solving a particular problem without specialized knowledge of the problem domain. The only knowledge the application needs is a training set that the network …
Implementation Of Numerically Stable Hidden Markov Model, Usha Ramya Tatavarty
Implementation Of Numerically Stable Hidden Markov Model, Usha Ramya Tatavarty
UNLV Theses, Dissertations, Professional Papers, and Capstones
A Hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. HMM is an extremely flexible tool and has been successfully applied to a wide variety of stochastic modeling tasks. One of the first applications of HMM is speech recognition. Later they came to be known for their applicability in handwriting recognition, part-of-speech tagging and bio-informatics.
In this thesis, we will explain the mathematics involved in HMMs and how to efficiently perform HMM computations using dynamic programming (DP) which makes it easy to implement …