The example is from learning dynamics of gaze patterns. In general, MLP works for static cases. I use a multi-layer recurrent network which accounts for the sequential patterns. Such a network can learn to recognize or predict several functions at once with certain limitations. Welcome to addittional questions!
The program classifies whatever sequential patterns and predicts next states. In this example, you see a somwhat successful simulation of recognition of gaze patterns. This is a recurrent network. It accounts for the time series of the function. It can learn more than one function. Wlcome for further questions!
The example is from learning dynamics of gaze patterns. In general, MLP works for static cases. I use a multi-layer recurrent network which accounts for the sequential patterns. Such a network can learn to recognize or predict several functions at once with certain limitations. Welcome to addittional questions!
Emilianlalev 1 year ago
The program classifies whatever sequential patterns and predicts next states. In this example, you see a somwhat successful simulation of recognition of gaze patterns. This is a recurrent network. It accounts for the time series of the function. It can learn more than one function. Wlcome for further questions!
Emilianlalev 1 year ago
This is for example somebody who learned to recognize a song by its fragments as well as to predict what is coming next in the song...
Emilianlalev 3 years ago