Result: Derivative free stochastic discrete gradient method with adaptive mutation

Title:
Derivative free stochastic discrete gradient method with adaptive mutation
Authors:
GHOSH, Ranadhir, GHOSH, Moumita, BAGIROV, Adil
Source:
Advances in data mining (applications in medicine, web mining, marketing, image and signal mining)0ICDM 2006. :264-278
Publisher Information:
Berlin; New York: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 6 ref 1
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, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme randomisé, Randomized algorithm, Algoritmo aleatorizado, Algorithme recherche, Search algorithm, Algoritmo búsqueda, Analyse donnée, Data analysis, Análisis datos, Classification, Clasificación, Estimation paramètre, Parameter estimation, Estimación parámetro, Extraction information, Information extraction, Extracción información, Fouille donnée, Data mining, Busca dato, Identification système, System identification, Identificación sistema, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Modélisation, Modeling, Modelización, Méthode adaptative, Adaptive method, Método adaptativo, Méthode gradient, Gradient method, Método gradiente, Méthode stochastique, Stochastic method, Método estocástico, Optimisation sans contrainte, Unconstrained optimization, Optimización sin restricción, Programmation mathématique, Mathematical programming, Programación matemática, Programmation non convexe, Non convex programming, Programación no convexa, Recherche locale, Local search, Busca local, Solution globale, Global solution, Solución global, Temps recherche, Search time, Tiempo búsqueda
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Information Technology and Mathematical Sciences, University of Ballarat, P.O. Box 663, Ballarat 3353, Australia
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19131449
Rights:
Copyright 2007 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.19131449
Database:
PASCAL Archive

Further Information

In data mining we come across many problems such as function optimization problem or parameter estimation problem for classifiers for which a good learning algorithm for searching is very much necessary. In this paper we propose a stochastic based derivative free algorithm for unconstrained optimization problem. Many derivative-based local search methods exist which usually stuck into local solution for non-convex optimization problems. On the other hand global search methods are very time consuming and works for only limited number of variables. In this paper we investigate a derivative free multi search gradient based method which overcomes the problems of local minima and produces global solution in less time. We have tested the proposed method on many benchmark dataset in literature and compared the results with other existing algorithms. The results are very promising.