Treffer: 1-Norm extreme learning machine for regression and multiclass classification using Newton method

Title:
1-Norm extreme learning machine for regression and multiclass classification using Newton method
Authors:
BALASUNDARAM, S, GUPTA, Deepak, KAPIL
Source:
Neurocomputing (Amsterdam). 128:4-14
Publisher Information:
Amsterdam: Elsevier, 2014.
Publication Year:
2014
Physical Description:
print, 40 ref
Original Material:
INIST-CNRS
Subject Terms:
Cognition, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Programmation mathématique, Mathematical programming, 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, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme rapide, Fast algorithm, Algoritmo rápido, Boucle anticipation, Feedforward, Ciclo anticipación, Classification à vaste marge, Vector support machine, Máquina ejemplo soporte, Etude expérimentale, Experimental study, Estudio experimental, Fonction base radiale, Radial basis function, Función radial base, Fonction décision, Decision function, Función decisión, Généralisation, Generalization, Generalización, Modélisation, Modeling, Modelización, Méthode Newton, Newton method, Método Newton, Méthode pénalité, Penalty method, Método penalidad, Optimisation sans contrainte, Unconstrained optimization, Optimización sin restricción, Programmation linéaire, Linear programming, Programación lineal, Représentation parcimonieuse, Sparse representation, Representación parsimoniosa, Réseau neuronal, Neural network, Red neuronal, Solution optimale, Optimal solution, Solución óptima, Classification multiple, Multiple classification, clasificación múltiple, Machine d'apprentissage extrême, Extreme learning machine, Máquina de Aprendizado Extremo, Réseau neuronal non bouclé, Feedforward neural nets, Red neural unidireccional, Dual exterior penalty problem, Feedforward neural networks, Linear programming problem
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India
ISSN:
0925-2312
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28284236
Rights:
Copyright 2015 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

Operational research. Management

Telecommunications and information theory
Accession Number:
edscal.28284236
Database:
PASCAL Archive

Weitere Informationen

In this paper, a novel 1-norm extreme learning machine (ELM) for regression and multiclass classification is proposed as a linear programming problem whose solution is obtained by solving its dual exterior penalty problem as an unconstrained minimization problem using a fast Newton method. The algorithm converges from any starting point and can be easily implemented in MATLAB. The main advantage of the proposed approach is that it leads to a sparse model representation meaning that many components of the optimal solution vector will become zero and therefore the decision function can be determined using much less number of hidden nodes in comparison to ELM. Numerical experiments were performed on a number of interesting real-world benchmark datasets and their results are compared with ELM using additive and radial basis function (RBF) hidden nodes, optimally pruned ELM (OP-ELM) and support vector machine (SVM) methods. Similar or better generalization performance of the proposed method on the test data over ELM, OP-ELM and SVM clearly illustrates its applicability and usefulness.