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» On Combining Dissimilarity Representations
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MCS
2001
Springer
13 years 11 months ago
On Combining Dissimilarity Representations
For learning purposes, representations of real world objects can be built by using the concept of dissimilarity (distance). In such a case, an object is characterized in a relative...
Elzbieta Pekalska, Robert P. W. Duin
MCS
2000
Springer
13 years 10 months ago
Combining Fisher Linear Discriminants for Dissimilarity Representations
Abstract Investigating a data set of the critical size makes a classification task difficult. Studying dissimilarity data refers to such a problem, since the number of samples equa...
Elzbieta Pekalska, Marina Skurichina, Robert P. W....
ICML
2007
IEEE
14 years 7 months ago
Learning to combine distances for complex representations
The k-Nearest Neighbors algorithm can be easily adapted to classify complex objects (e.g. sets, graphs) as long as a proper dissimilarity function is given over an input space. Bo...
Adam Woznica, Alexandros Kalousis, Melanie Hilario
PRL
2002
95views more  PRL 2002»
13 years 6 months ago
Dissimilarity representations allow for building good classifiers
In this paper, a classification task on dissimilarity representations is considered. A traditional way to discriminate between objects represented by dissimilarities is the neares...
Elzbieta Pekalska, Robert P. W. Duin
WSC
1997
13 years 8 months ago
Covalidation of Dissimilarly Structured Models
A methodology is presented which allows comparison between models under different modeling paradigms. Consider the following situation: Two models have been constructed to study d...
Samuel A. Wright, Kenneth W. Bauer Jr.