In one of the final assignments, we were individually asked to apply and evaluate back-propagation in solving several types of problems that include classification, function estimation, and time-series prediction. I then built a series of tests to evaluate the effectiveness of "Back Prop" configurations for each specific problem.
Although the goal was to ultimately solve each problem, setting-up and refactoring the problem to work under the constraints of neural networks was also a rewarding task in itself. In this classification problem, the goal is to identify whether a certain "data point" belongs to Class 1, 2, or 3 see above. Random points are assigned to a certain class, and the neural network is trained to find the pattern.
When training is complete, it will use what it has learned to accurately classify new points. In this problem, the network tries to approximate the value of a certain function. It is fed with noisy data, and the goal is to find the true pattern.
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- Applications of Error Back-Propagation to Phonetic Classification - Semantic Scholar!
After training, the network successfully estimates the value of the gaussian function below. In this problem, the goal is to design a neural network to predict a value based on a given time-series data i. To approach this problem, the inputs to the neural network have to be refactored in chunks, and the resulting output will be the next data item directly following that chunk see below.
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Muthusamy , Angry Ronald , Malcolm. View 2 Excerpts. High quality speech for laryngectomized persons Harina Kapoor. Modularity and scaling in large phonemic neural networks. IEEE Trans. References Publications referenced by this paper.
- Applications of Error Back-Propagation to Phonetic Classification.
- Applications for Back-propagation Learning: Neural Networks in Action!
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