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Center for Neuromorphic Systems Engineering
Research: Yaser Abu-Mostafa
Click on full report to go to detailed report; click on author name to go to home page (or email).
 

Distributed Learning in Swarm Systems
Ling Li, Alcherio Martinoli, Yaser Abu-Mostafa

Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent may have only partial information about the environment and other agents. We model the system and individual agents, then use several techniques such as reinforcement learning to find the optimal strategy for each agent in order to maximize the group performance. Our experiments with the stick-pulling problem showed agents became specialized automatically.
(full report)
 

A Priori Training Data Valuation
Alexander Nicholson, Yaser Abu-Mostafa

For machine learning it is generally accepted that a greater amount of available data facilitates improved generalization. In practice, however, a learning algorithm cannot accomodate and unlimited data set and may be hindered by noisy and irregular data. We introduce a procedure for evaluating individual training examples. This valuation can serve as a basis for selecting training sets of limited size and for detecting outliers or other undesirable data. We demonstrate that learning with a data set from which the worst data has been removed can result in improved generalization performance. (full report)

The Bin Model for Generalization
Alexander Nicholson, Xubo Song, Yaser Abu-Mostafa

The problem of overfitting the data is attacked by using the Bin Model analysis. This provides a method of bounding generalization error without sacrificing valuable training data. (full report)

 

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last modified: 2/22/07