Campus Event Calendar
Event Entry
What and Who
"Learning with Neural Networks: Importance of the VC-Dimension and Methods of Bounding the VC-Dimension"
Frauke Friedrichs
Talk
AG 1, AG 2, AG 3, AG 4
MPI Audience
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Date, Time and Location
Wednesday, 27 November 2002
14:00
-- Not specified --
46.1 - MPII
024
Saarbrücken
Abstract
I will explain what a neural network is and how it is possible to
learn with neural networks. I will focus on neural networks with a
binary output space and will give a corresponding definition of a
learning algorithm (agnostic PAC learning).
In this context the VC-Dimension is an important quantity. It is
often difficult to calculate the VC-dimension exactly. Therefore
I want to explain how one can derive upper and lower bounds on the
VC-dimension. For the upper bound we use techniques that count
the number of connected components in the partition of parameter
space defined by the input points.
learn with neural networks. I will focus on neural networks with a
binary output space and will give a corresponding definition of a
learning algorithm (agnostic PAC learning).
In this context the VC-Dimension is an important quantity. It is
often difficult to calculate the VC-dimension exactly. Therefore
I want to explain how one can derive upper and lower bounds on the
VC-dimension. For the upper bound we use techniques that count
the number of connected components in the partition of parameter
space defined by the input points.
Contact
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