Title :
Probabilistic Mercer Kernel Clusters
Author :
Yang, Zheng Rong
Author_Institution :
Dept. of Comput. Sci., Exeter Univ.
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;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1614993
