DocumentCode
1567066
Title
Probabilistic Mercer Kernel Clusters
Author
Yang, Zheng Rong
Author_Institution
Dept. of Comput. Sci., Exeter Univ.
Volume
3
fYear
2005
Firstpage
1885
Lastpage
1890
Abstract
Cluster analysis is one of the most important areas in machine learning. Most clustering algorithms are working in the Euclidean space, where the basic requisite is that each cluster has a hyper-ellipsoidal distribution. It has been recognized that this restriction may not be satisfied in many applications and some new ideas have been proposed (S. J. Roberts, et al., 1999), (M. Girolami, 2002). This paper investigates the construction of probabilistic Mercer kernel clusters using the maximum likelihood training procedure
Keywords
learning (artificial intelligence); maximum likelihood estimation; pattern clustering; probability; Euclidean space; cluster analysis; hyper-ellipsoidal distribution; machine learning; maximum likelihood training; probabilistic Mercer kernel clusters; Bayesian methods; Buildings; Clustering algorithms; Computer science; Kernel; Machine learning; Machine learning algorithms; Maximum likelihood estimation; Partitioning algorithms; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location
Beijing
Print_ISBN
0-7803-9422-4
Type
conf
DOI
10.1109/ICNNB.2005.1614993
Filename
1614993
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