Paper Info
Title
Correspondences Between Fuzzy Equivalence Relations And Kernels: Theoretical Results And Potential Applications
Abstract
Kernels have proven useful for machine learning, data mining, and computer vision as they provide a means to derive non-linear variants of learning, optimization or classification strategies from linear ones. A central question when applying a kernel-based method is the choice and the design of the kernel function. This paper provides a novel view on kernels based on fuzzy logical concepts that allows to incorporate prior knowledge in the design process. It is demonstrated that kernels that map to the unit interval and have constantly I in their diagonals can be represented by a commonly used fuzzy-logical formula for representing fuzzy relations. This means that a large and important class of kernels can be represented by fuzzy logical concepts. Beside this result which only guarantees the existence of such a representation, constructive examples are presented.
Year
DOI
Venue
2006
10.1109/FUZZY.2006.1682001
2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5
Keywords
Field
DocType
design process,machine learning,fuzzy set theory,kernel function,computer vision,fuzzy logic,data mining
Kernel (linear algebra),Diagonal,Constructive,Computer science,Fuzzy logic,Unit interval,Fuzzy set,Engineering design process,Artificial intelligence,Machine learning,Kernel (statistics)
Conference
ISSN
Citations 
PageRank 
1098-7584
0
0.34
References 
Authors
8
2
Name
Order
Citations
PageRank
Bernhard Moser100.34
Ulrich Bodenhofer270568.02