Result: Performance comparison between backpropagation, neuro-fuzzy network, and SVM
Title:
Performance comparison between backpropagation, neuro-fuzzy network, and SVM
Authors:
Source:
Computer science (theory and applications)0CSR 2006. :438-446
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 9 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, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, 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, Sciences biologiques et medicales, Biological and medical sciences, Sciences medicales, Medical sciences, Tumeurs, Tumors, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme flou, Fuzzy algorithm, Algoritmo borroso, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Analyse statistique, Statistical analysis, Análisis estadístico, Apprentissage probabilités, Probability learning, Aprendizaje probabilidades, Base donnée, Database, Base dato, Cancérologie, Cancerology, Cancerología, Glande mammaire, Mammary gland, Glándula mamaria, Homme, Human, Hombre, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Logique floue, Fuzzy logic, Lógica difusa, Machine exemple support, Vector support machine, Máquina ejemplo soporte, Réseau neuronal flou, Fuzzy neural nets, Réseau neuronal, Neural network, Red neuronal, Rétropropagation, Backpropagation, Retropropagacíon, Système réparti, Distributed system, Sistema repartido, Tumeur maligne, Malignant tumor, Tumor maligno, Validation croisée, Cross validation, Validación cruzada
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Computer Engineering, Sejong University, Seoul, Korea, Republic of
Dept. of Electrical Engineering, Korea University, Seoul, Korea, Republic of
Dept. of Electrical Engineering, Korea University, Seoul, Korea, Republic of
ISSN:
0302-9743
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
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
Tumours
Tumours
Accession Number:
edscal.19150270
Database:
PASCAL Archive
Further Information
In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.