Performance comparison of RBF networks and MLPs for classification

Authors:
Hyontai Sug
Affiliations:
Division of Computer and Information Engineering, Dongseo University, Busan, Republic of Korea
Venue:
AIC'09 Proceedings of the 9th WSEAS international conference on Applied informatics and communications
Year:
2009

Citing 15
Cited 1

Perceptrons: expanded edition

Perceptrons: expanded edition
Effects of Sample Size in Classifier Design

IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial intelligence: a modern approach

Artificial intelligence: a modern approach
Efficient progressive sampling

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm

Neural Networks
Radial basis function networks 1: recent developments in theory and applications

Radial basis function networks 1: recent developments in theory and applications
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Artificial Neural Networks for Document Analysis and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
2008 Special Issue: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance

Neural Networks
Data Mining Methods and Models

Data Mining Methods and Models
Adaptive document block segmentation and classification

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary optimization of radial basis function classifiers for data mining applications

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Probabilistic neural-network structure determination for pattern classification

IEEE Transactions on Neural Networks

Property of artificial neural networks of classification with respect to training set size

ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume II

Quantified Score

Hi-index	0.00

Visualization

Abstract

The two well-known neural network algorithms, multilayer perceptrons and radial basis function networks, have different structures and characteristics, so that they have different performance in classification tasks depending on the available training data sets. This paper comparares the performance of the two neural networks with respect to training data set size in classification tasks. Experiments using two real world data sets with the two neural network algorithms show that multilayer perceptrons have relatively better performance for larger data sets and radial basis function networks have relatively better performance for smaller data sets.