Current Slide
Small screen detected. You are viewing the mobile version of SlideWiki. If you wish to edit slides you will need to use a larger device.
Back-Propagation Learning
- The errors (and therefore the learning) propagate backwards from the output layer to the hidden layers.
-
Learning at the output layer is the same as for single-layer perceptron:
- Wj ← Wj + α × Err × g'(in) × xj
- Hidden layer neurons get a "blame" assigned for the error (back-propagation of error), giving greater responsibility to neurons connected by stronger weight.
- Back-propagation of error updates the weights of the hidden layer; the principle thus stays the same.
Speaker notes:
Content Tools
Tools
Sources (0)
Tags (0)
Comments (0)
History
Usage
Questions (0)
Playlists (0)
Quality
Sources
There are currently no sources for this slide.