See this document in CiteSeerX!

The Optimal Number of Learning Samples and Hidden Units in Function Approximation With a Feedforward Network (1993)  (Make Corrections)  
Vytautas Vysniauskas, Frans C.A. Groen, Ben J.A. Kröse



  Home/Search   Context   Related

 
View or download:
wins.uva.nl/pub/co...VysGroKro93b.ps.gz
wins.uva.nl/pub/co...VysGroKro93b.ps.gz
Cached:  PS.gz  PS  PDF   Image  Update  Help

From:  wins.uva.nl/resear...publications (more)
(Enter author homepages)

Rate this article: (best)
  Comment on this article  
(Enter summary)

Abstract: This paper presents a methodology to estimate the optimal number of learning samples and the number of hidden units needed to obtain a desired accuracy of a function approximation by a feedforward network. The representation error and the generalization error, components of the total approximation error are analyzed and the approximation accuracy of a feedforward network is investigated as a function of the number of hidden units and the number of learning samples. Based on the asymptotical... (Update)

Active bibliography (related documents):   More   All
0.5:   Minimisation Methods for Training Feed-Forward Neural Networks - van der Smagt (1992)   (Correct)
0.5:   VC Dimension and Learnability of Sparse Polynomials and.. - Karpinski, Werther (1989)   (Correct)
0.1:   A Real-Time Learning Neural Robot Controller - van der Smagt, Kröse (1991)   (Correct)

Similar documents based on text:   More   All
0.4:   Orthogonal Incremental Learning of a Feedforward Network - Vysniauskas, Groen, Kröse (1995)   (Correct)
0.3:   Multiplier-Free Feedforward Networks - Khan   (Correct)
0.3:   Capacity of Structured Multilayer Networks with Shared Weights - Kröner, Moratz   (Correct)

BibTeX entry:   (Update)

@misc{ vysniauskas-optimal,
  author = "Vytautas Vysniauskas and Frans C.A. Groen and Ben J.A. Kröse",
  title = "The Optimal Number of Learning Samples and Hidden Units in Function Approximation
    With a Feedforward Network",
  url = "citeseer.ist.psu.edu/vysniauskas93optimal.html" }
Citations (may not include all citations):
1527   Optimization by Simulated Annealing - Kirkpatrick, Gelatt et al. - 1983
623   Multilayer Feedforward Networks are Universal Approximators (context) - Hornik, Stinchcombe et al. - 1989
454   the uniform convergence of relative frequencies of events to.. (context) - Vapnik, Chervonenkis et al. - 1971
357   Approximation by Superpositions of a Sigmoidal Function (context) - Cybenko - 1989
348   Estimation of dependences based on empirical data (context) - Vapnik - 1982
340   Learning representation by back--propagating errors (context) - Rumelhart, Hinton et al. - 1986
235   the Approximate Realization of Continuous Mappings by Neural.. (context) - Funahashi - 1989
139   Stochastic complexity and modeling (context) - Rissanen - 1986
99   Learning in Artificial Neural Networks: A Statistical Perspe.. (context) - White - 1989
89   Approximation and estimation bounds for artificial neural ne.. (context) - Barron - 1991
85   Simplifying Neural Networks by Soft WeightSharing (context) - Nowlan, Hinton - 1992
64   Connectionist Nonparametric Regression: Multilayer Feedforwa.. (context) - White - 1990
63   What Size Net Gives Valid Generalization (context) - Baum, Haussler - 1989
58   Statistical mechanics of learning from examples (context) - Seung, Sompolinsky et al. - 1992
47   Complexity Regularization with Application to Neural Network.. (context) - Barron - 1991
44   A Statistical Approach to Learning and Generalization in Lay.. (context) - Levin, Thishby et al. - 1990
39   Restart procedures for the conjugate gradient method (context) - Powell - 1977
17   A New Approach for Finding the Global Minimum of Error Funct.. (context) - Baba - 1989
9   Learning strategies for a vision based neural controller for.. (context) - Krose, van der Korst et al. - 1990
2   the Geometry of Feedforward Neural Network Weight Spaces (context) - Chen, Hecht-Nielsen et al. - 1991
2   Generalizing the PAC Model: Sample Size Bounds From Metric D.. (context) - Hausler - 1989
1   A one layer feedback, artificial network with a Bayesian lea.. (context) - Lansen, Ekeberg - 1989
1   Control of the UvA OSCAR-6 robot (context) - van der Smagt - 1991

Documents on the same site (http://www.wins.uva.nl/research/ias/publications/publications.html):   More
Observer Curve and Object Detection from the Optic Flow - Dev, Kröse, Dorst, Groen   (Correct)
Recovering Patch Parameters from the Optic Flow with Auto.. - Dev, Kröse, Groen   (Correct)
Interpolative Robot Control With The Nested Network Approach - van der Smagt, Jansen.. (1992)   (Correct)

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC