Citation
Fyfe, William John Andrew (1992) Invariance hints and the VC dimension. Dissertation (Ph.D.), California Institute of Technology. https://resolver.caltech.edu/CaltechETD:etd07202007075240
Abstract
We are interested in having a neural network learn an unknown function f. If the function satisfies an invariant of some sort, such as f is an odd function, then we want to be able to take advantage of this information and not have the network deduce the invariant based on an example of f.
The invariant might be defined in terms of an explicit transformation of the input space under which f is constant. In this case it is possible to build a network that necessarily satisfies the invariant.
In general, we define the invariant in terms of a partition of the input space such that if x, x' are in the same partition element then f(x) = f(x'). An example of the invariant would be a a pair (x, x') taken from a single partition element. We can combine examples of the invariant with examples of the function in the learning process. The goal is to substitute examples of the invariant for examples of the function; the extent to which we can actually do this depends on the appropriate VC dimensions. Simulations verify, at least in simple cases, that examples of the invariant do aid the learning process.
Item Type:  Thesis (Dissertation (Ph.D.)) 

Degree Grantor:  California Institute of Technology 
Division:  Engineering and Applied Science 
Major Option:  Computer Science 
Thesis Availability:  Restricted to Caltech community only 
Research Advisor(s): 

Thesis Committee: 

Defense Date:  26 May 1992 
Record Number:  CaltechETD:etd07202007075240 
Persistent URL:  https://resolver.caltech.edu/CaltechETD:etd07202007075240 
Default Usage Policy:  No commercial reproduction, distribution, display or performance rights in this work are provided. 
ID Code:  2950 
Collection:  CaltechTHESIS 
Deposited By:  Imported from ETDdb 
Deposited On:  20 Jul 2007 
Last Modified:  03 Oct 2019 23:22 
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