1. Brian Merrick 3-25-10 CS498 Seminar

2.  Introduction to Neural Networks  Types of Neural Networks  Neural Networks with Pattern Recognition  Applications of Neural Networks  Conclusion  Questions

3.  Inspiration for development came from attempts to model the human central nervous system  Artificial network that simulates systems, such as how the brain processes information  Composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems

4. Each neural network consists of many input nodes whose input can to go one or more processing nodes to produce output

5.  Uses learning algorithms to compute output values based on result of the previous populations  Not rule-based like a traditional system, but trained to recognize and generalize the relationship between a set of inputs and outputs

6.  Prediction  Classification  Data Filtering  Supervised Learning  Unsupervised Learning

7.  Prediction Use input values to predict some output (e.g. pick the best stocks in the market, predict weather, identify people with cancer risks etc.) Example Networks:  Directed Random Search

8.  Classification Use input values to determine the classification (e.g. is the input the letter A, is the blob of video data a plane and what kind of plane is it) Example Networks:  Learning Vector Quantization  Probabilistic Neural Networks

9.  Data Filtering Smooth an input signal (e.g. take the noise out of a telephone signal) Example Networks:  Recirculation

10.  Uses a known structure with random weights. The inputs and outputs are known  The data set is large enough to complete learning and can be tested later for accuracy of computed outputs  The network adjusts weight values to some predetermined level of accuracy and then stops

11.  Seeks to determine how the data is organized  An answer is requested from the neural network and weights are adjusted if the answer is not ‘correct’  Uses back-propagation for each iteration in the network

12.  To begin, the network is initialized, all the connection strengths are set randomly, and the network sits as a blank slate  The network is then presented with information and the input nodes receive a digitized version of the image

13. In a gender pattern recognizer each response will be compared to the correct response for that picture (i.e., 0.0 for male, 1.0 for female) and each connection strength is adjusted so that next time it's shown that picture

14.  Character Recognition: handwriting recognition, number recognition  Image (Data) Compression: can compress and decompress image data  Pattern Recognition: rare coin evaluation, bomb sensing equipment in airports

15.  Signal Processing: removing telephone background noise, detecting engine misfires by sound in real-time  Finance: market forecasting, credit history checks, loan approval, telemarketing  Systems Control: factories, refineries, NASA space shuttle, robotics

16.  Neural networks try to simulate tasks of the human brain  Neural networks are very complex to implement because of the relationships between the data and the learning nodes  Unsupervised neural networks are the closest solution to modeling the human brain

18.  Neural Networks. http://www.doc.ic.ac.uk/~nd/surprise_96/jour nal/vol4/cs11/report.html#Introduction%20to% 20neural%20networks  Artificial Neural Networks Technology. http://nature.berkeley.edu/~bingxu/UU/ geocomp/Week14/Network%20Selection%205_0.htm  The Neural Approach to Pattern Recognition. http://www.acm.org/ubiquity/views/v5i7_jesan.html