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home · events · colloquia · 2006-2007 · 

Colloquium - Lee

ECCR 265

Learning in Artificial Sensorimotor Systems
University of Pennsylvania
Daniel Lee photo

Many algorithms in machine learning involve changing the underlying dimensionality of the data set. Unsupervised learning techniques such as principal components analysis typically involve dimensionality reduction, whereas supervised learning techniques such as support vector machines can be understood as mapping the data to a higher dimensional space. Equivalent problems emerge when considering information processing in sensorimotor systems. Sensory processing requires mapping high-dimensional sensory inputs onto a smaller number of perceptually-relevant features, whereas motor learning involves driving a large number of actuator parameters with a smaller number of control variables. I will describe some of our recently developed learning algorithms that utilize changes in dimensionality, and demonstrate their application on some novel robotic systems.

Hosted by Gregory Grudic.

The Department holds colloquia throughout the Fall and Spring semesters. These colloquia, open to the public, are typically held on Thursday afternoons, but sometimes occur at other times as well. If you would like to receive email notification of upcoming colloquia, subscribe to our Colloquia Mailing List. If you would like to schedule a colloquium, see Colloquium Scheduling.

Sign language interpreters are available upon request. Please contact Stephanie Morris at least five days prior to the colloquium.

See also:
Department of Computer Science
College of Engineering and Applied Science
University of Colorado Boulder
Boulder, CO 80309-0430 USA
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