A new incremental method for function approximation using feed-forward neural networks

No abstract is available for this paper.

• Publication year

  2002

• Venue

  Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

• Publication date

  2002-08-07

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/IJCNN.2002.1007821
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Functional Analysis

  2023cited by this paper
• Matching Pursuits を用いた音声分離法の改良

  2004cited by this paper
• Heterogeneous neural networks: theory and applications

  2000cited by this paper
• Incremental Approximation by Layer Neural Networks

  2000cited by this paper
• Neural networks with periodic and monotonic activation functions: a comparative study in classification problems

  1999cited by this paper
• A Sequential Learning Approach for Single Hidden Layer Neural Networks

  1998cited by this paper
• An Improvement of AdaBoost to Avoid Overfitting

  1998cited by this paper
• Introductory Functional Analysis: With Applications to Boundary Value Problems and Finite Elements

  1997cited by this paper
• Investigation of the CasCor Family of Learning Algorithms

  1997cited by this paper
• Heterogeneous radial basis function networks

  1996cited by this paper
• Incremental approximation by one-hidden-layer neural networks: discrete functions rapprochement

  1996cited by this paper
• The cascade-correlation learning: a projection pursuit learning perspective

  1996cited by this paper
• Experiments with a New Boosting Algorithm

  1996cited by this paper
• Neural networks for pattern recognition

  1995cited by this paper
• Regression modeling in back-propagation and projection pursuit learning

  1994cited by this paper
• Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization

  1993cited by this paper
• Multilayer feedforward networks with a nonpolynomial activation function can approximate any function

  1993cited by this paper
• Matching pursuits with time-frequency dictionaries

  1993cited by this paper
• Approximation and Radial-Basis-Function Networks

  1993cited by this paper
• Universal approximation bounds for superpositions of a sigmoidal function

  1993cited by this paper
• Global Optimization: Deterministic Approaches

  1992cited by this paper
• A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training

  1992cited by this paper
• Stable solvers and block elimination for bordered systems

  1991cited by this paper
• The Cascade-Correlation Learning Architecture

  1989cited by this paper
• What is projection pursuit

  1987cited by this paper
• An introduction to nonharmonic Fourier series

  1980cited by this paper
• Approximation of Functions.

  1968cited by this paper
• Theory of Approximation

  1960cited by this paper

• The Convergence of Incremental Neural Networks

  2023cites this paper
• A Novel Kernel-based Extreme Learning Machine with Incremental Hidden Layer Nodes

  2020cites this paper
• Health care predictive analytics using artificial intelligence techniques

  2018cites this paper
• Benchmarking the selection of the hidden-layer weights in extreme learning machines

  2017cites this paper
• Sine neural network (SNN) with double-stage weights and structure determination (DS-WASD)

  2014cites this paper
• Further studies of extreme learning machine and compressed signal detection.

  2013cites this paper
• A modified extreme learning machine with sigmoidal activation functions

  2013cites this paper
• Comparing error minimized extreme learning machines and support vector sequential feed-forward neural networks

  2012cites this paper
• Optimization approximation solution for regression problem based on extreme learning machine

  2011cites this paper
• A study on effectiveness of extreme learning machine

  2011cites this paper
• A Modified Extreme Learning Machine

  2011cites this paper
• A Modied Extreme Learning Machine

  2011cites this paper
• Composite function wavelet neural networks with extreme learning machine

  2010cites this paper
• Comparing Error Minimized Extreme Learning Machines and Support Vector Sequential Feedforward Neural Networks for Classification Problems

  2010cites this paper
• Small Number of Hidden Units for ELM with Two-Stage Linear Model

  2008cites this paper
• Convergence analysis of convex incremental neural networks

  2008cites this paper
• Outlier Treatment for SLFNs in Classification

  2007influential citation
• Incremental extreme learning machine.

  2007influential citation
• Mesh optimizatin : Anisotropic or Uniform Meshes for Reaction Diffusion Problems?

  2007cites this paper
• Extreme learning machine: Theory and applications

  2006influential citation
• Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes

  2006cites this paper
• Penalty OBS scheme for feedforward neural network

  2005cites this paper
• Selection of Weights for Sequential Feed-Forward Neural Networks: An Experimental Study

  2005cites this paper
• A Novel RBF Neural Network with Fast Training and Accurate Generalization

  2004influential citation
• A Novel Fuzzy Neural Network with Fast Training and Accurate Generalization

  2004cites this paper
• Learning with Feed-forward Neural Networks: Three Schemes to Deal with the Bias/Variance Trade-off

  2004cites this paper
• Feature selection forcing overtraining may help to improve performance

  2003cites this paper
• Incremental construction of LSTM recurrent neural network

  2002cites this paper
• Evolving connectionist systems

  2002cites this paper