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» On Combining Dissimilarity Representations
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MCS
2001
Springer
13 years 9 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 9 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 6 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 5 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 6 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.