Paper Info
Title
A Rapid Graph-based Method for Arbitrary Transformation-Invariant Pattern Classification
Abstract
We present a graph-based method for rapid, accurate search through prototypes for transformation-invariant pattern classifica(cid:173) tion. Our method has in theory the same recognition accuracy as other recent methods based on ''tangent distance" [Simard et al., 1994], since it uses the same categorization rule. Nevertheless ours is significantly faster during classification because far fewer tan(cid:173) gent distances need be computed. Crucial to the success of our system are 1) a novel graph architecture in which transformation constraints and geometric relationships among prototypes are en(cid:173) coded during learning, and 2) an improved graph search criterion, used during classification. These architectural insights are applica(cid:173) ble to a wide range of problem domains. Here we demonstrate that on a handwriting recognition task, a basic implementation of our system requires less than half the computation of the Euclidean sorting method.
Year
Venue
Field
1994
NIPS
Graph,Categorization,Computer science,Handwriting recognition,Sorting,Tangent,Artificial intelligence,Invariant (mathematics),Euclidean geometry,Machine learning,Computation
DocType
Citations 
PageRank 
Conference
4
2.25
References 
Authors
4
2
Name
Order
Citations
PageRank
Alessandro Sperduti11605137.88
David G. Stork2627106.17