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Learning Algorithms of Neural Networks and Applications

Learning Algorithms of Neural Networks and Applications. Jonathan Reagan Umass Dartmouth CSUMS Summer 11 August 3 rd 2011. Outline. What is a Neural Network? How does it work? Why do we care? Results Issues encountered Future work. Neural Network. Input Layers. Output Layers.

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Learning Algorithms of Neural Networks and Applications

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  1. Learning Algorithms of Neural Networks and Applications Jonathan Reagan Umass Dartmouth CSUMS Summer 11 August 3rd 2011

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

  3. Neural Network Input Layers Output Layers Hidden Layers

  4. Idea of Learning Not realistic to study every possible case Smaller sample can be used to model the entire case Assume connections hold

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

  6. Learning Methods-Cont. Use the learning method to find α I Y-Xα I=0

  7. Preliminary Results

  8. Speed of Convergence

  9. Speed of Convergence-cont.

  10. Why have limits on Iterations?

  11. Data Testing 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

  12. Future Work Increase the speed of the Neural Network Find more applicable data for testing of the Neural Network Try multiple layer Neural Networks and Compare

  13. Questions?

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