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Classification Part 3: Artificial Neural Networks

Classification Part 3: Artificial Neural Networks. BMTRY 726 4/15/14. Last Class. Last class we discussed (1) Problems with linear classification methods -As with regression, we have a hard time including a large number of covariates especially if n is small

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Ch4: Backpropagation (BP)

Werbos -> Ponker -> Rummelhart -> McClelland. Ch4: Backpropagation (BP). ? BP Architecture:. Characteristics: Multilayer, feedforward, fully connected. ? Potential problems being solved by BP 1. Data translation, e.g., data compression

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356 views • 35 slides

Friday, 21 November 2003 William H. Hsu Department of Computing and Information Sciences, KSU

Lecture 36. Applications 2 of 3: Signal Processing and Pattern Recognition. Friday, 21 November 2003 William H. Hsu Department of Computing and Information Sciences, KSU http://www.kddresearch.org http://www.cis.ksu.edu/~bhsu Readings: Sections 4.5-4.9, Mitchell

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362 views • 12 slides

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Hidden layer weights - PowerPoint PPT Presentation

Classification Part 3: Artificial Neural Networks

Ch4: Backpropagation (BP)

Friday, 21 November 2003 William H. Hsu Department of Computing and Information Sciences, KSU