Training a reciprocal-sigmoid classifier by feature scaling-space
Machine Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Incremental constructive ridgelet neural network
Neurocomputing
No-reference image quality assessment using modified extreme learning machine classifier
Applied Soft Computing
How many hidden layers and nodes?
International Journal of Remote Sensing
Real-Time Collaborative Filtering Using Extreme Learning Machine
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
QoS-aware roadside base station assisted routing in vehicular networks
Engineering Applications of Artificial Intelligence
Error minimized extreme learning machine with growth of hidden nodes and incremental learning
IEEE Transactions on Neural Networks
Letters: Fully complex extreme learning machine
Neurocomputing
Extreme support vector machine classifier
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A new online learning algorithm for structure-adjustable extreme learning machine
Computers & Mathematics with Applications
Engineering Applications of Artificial Intelligence
A supervised combination strategy for illumination chromaticity estimation
ACM Transactions on Applied Perception (TAP)
Constructive approximation to multivariate function by decay RBF neural network
IEEE Transactions on Neural Networks
Two-stage extreme learning machine for regression
Neurocomputing
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Comparative study of extreme learning machine and support vector machine
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs
Knowledge-Based Systems
Displacement prediction model of landslide based on ensemble of extreme learning machine
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Comparing studies of learning methods for human face gender recognition
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Predicting time series of railway speed restrictions with time-dependent machine learning techniques
Expert Systems with Applications: An International Journal
Meta-ELM: ELM with ELM hidden nodes
Neurocomputing
Fast sparse approximation of extreme learning machine
Neurocomputing
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The problem of the necessary complexity of neural networks is of interest in applications. In this paper, learning capability and storage capacity of feedforward neural networks are considered. We markedly improve the recent results by introducing neural-network modularity logically. This paper rigorously proves in a constructive method that two-hidden-layer feedforward networks (TLFNs) with 2√(m+2)N (≪N) hidden neurons can learn any N distinct samples (xi, ti) with any arbitrarily small error, where m is the required number of output neurons. It implies that the required number of hidden neurons needed in feedforward networks can be decreased significantly, comparing with previous results. Conversely, a TLFN with Q hidden neurons can store at least Q2/4(m+2) any distinct data (xi, ti) with any desired precision.