Master of Science in Computer Science
First Committee Member
Matt Pedersen, Chair
Second Committee Member
Third Committee Member
Graduate Faculty Representative
Number of Pages
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 will be trained with. The application uses a master-slave architecture to generate and select a neural network capable of solving a given problem.
Applied sciences; Clojure (Computer program language); JMS; Mathematical optimization; Network; Neural; Neural networks (Computer science); Optimize; Stochastic processes
Artificial Intelligence and Robotics | Computer Sciences | Statistical Methodology
Hurt, Jason Lee, "Automating construction and selection of a neural network using stochastic optimization" (2011). UNLV Theses, Dissertations, Professional Papers, and Capstones. 1258.