Treffer: Expectation-minimax approach to clustering analysis

Title:
Expectation-minimax approach to clustering analysis
Authors:
CHEUNG, Yiu-Ming
Source:
Artificial neural networks and neural information processing - ICANN / ICONIP 2003 (Istanbul, 26-29 June 2003)Lecture notes in computer science. :165-172
Publisher Information:
Berlin: Springer, 2003.
Publication Year:
2003
Physical Description:
print, 9 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme EM, EM algorithm, Algoritmo EM, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme compétitif, Competitive algorithms, Amas, Cluster, Montón, Analyse amas, Cluster analysis, Analisis cluster, Apprentissage, Learning, Aprendizaje, Fiabilité, Reliability, Fiabilidad, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Maximisation fonction, Function maximization, Maximización función, Méthode minimax, Minimax method, Método minimax, Problème minimax, Minimax problem, Problema minimax, Sélection automatique, Automatic selection, Selección automática
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science, Hong Kong Baptist University, Hong-Kong
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=15509829
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.15509829
Database:
PASCAL Archive

Weitere Informationen

This paper proposes a general approach named Expectation-MiniMax (EMM) for clustering analysis without knowing the cluster number. It describes the contrast function of Expectation-Maximization (EM) algorithm by an approximate one with a designable error term. Through adaptively minimizing a specific error term meanwhile maximizing the approximate contrast function, the EMM automatically penalizes all rivals during the competitive learning. Subsequently, the EMM not only includes the Rival Penalized Competitive Learning algorithm (Xu et al. 1993) and its Type A form (Xu 1997) with the new variants developed, but also provides a better alternative way to optimize the EM contrast function with at least two advantages: (1) faster model parameter learning speed, and (2) automatic model-complexity selection capability. We present the general learning procedures of the EMM, and demonstrate its outstanding performance in comparison with the EM.