Jonathan Reagan Umass Dartmouth CSUMS Summer 11 August 3 rd 2011.

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Presentation transcript:

Jonathan Reagan Umass Dartmouth CSUMS Summer 11 August 3 rd 2011

 What is a Neural Network?  How does it work?  Why do we care?  Results  Issues encountered  Future work

Input Layers Hidden Layers Output Layers

 Not realistic to study every possible case  Smaller sample can be used to model the entire case  Assume connections hold

 (input)=[age, income, credit score, etc]  (output)=[dependability]  We want weights of α’s  X* α (Hidden)=Y

 Use the learning method to find α  I Y-Xα I=0

PerceptronLeast Square NAccuracys Failed TrialsNAccuracys Failed Trials MinAVGMaxN/Total MinAVGMaxN/Total / / / / / / / / / / / / / / / / / /100

2 million Convergence Failed Trials 4 million convergence Failed Trials NT(Time)SecondsN/TotalNT(Time)SecondsN/Total / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /50

 Random Data can’t be learned  Deterministic Data can be learned  Adding Random variance decreases Accuracy  More values of N the Better  But more values of N take Longer

 Increase the speed of the Neural Network  Find more applicable data for testing of the Neural Network  Try multiple layer Neural Networks and Compare