Result: A new sequential algorithm for regression problems by using mixture distribution
Title:
A new sequential algorithm for regression problems by using mixture distribution
Authors:
Source:
ICANN 2002 : artificial neural networks (Madrid, 28-30 August 2002)Lecture notes in computer science. :535-540
Publisher Information:
Berlin: Springer, 2002.
Publication Year:
2002
Physical Description:
print, 8 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence non paramétrique, Nonparametric inference, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Simulation, Algorithme glouton, Greedy algorithm, Algoritmo glotón, Chauffe appoint, Boosting, Calentamiento complementario, Estimation non paramétrique, Non parametric estimation, Estimación no paramétrica, Méthode résidu pondéré, Weighted residual method, Método residuo pondero, Méthode séquentielle, Sequential method, Método secuencial
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
The Tokyo Institute of Technology, Ookayama 2-12-1, Meguro-ku, Tokyo 152-8552, Japan
ISSN:
0302-9743
Rights:
Copyright 2003 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
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
Mathematics
Mathematics
Accession Number:
edscal.14511330
Database:
PASCAL Archive
Further Information
A new sequential method for the regression problems is studied. The suggested method is motivated by boosting methods in the classification problems. Boosting algorithms use the weighted data to update the estimator. In this paper we construct a sequential estimation method from the viewpoint of nonparametric estimation by using mixture distribution. The algorithm uses the weighted residuals of training data. We compare the suggested algorithm to the greedy algorithm by the simple simulation.