# Neural Network Learning: Testing Bounds on Sample Complexity

### Joaquim Marques de Sá, Fernando Sereno, Luís Alexandre

#### Abstract

Several authors have theoretically determined distribution-free bounds on sample complexity. Formulas based on several learning paradigms have been presented. However, little is known on how these formulas perform and compare with each other in practice. To our knowledge, controlled experimental results using these formulas, and comparing of their behavior, have not so far been presented. The present paper represents a contribution to filling up this gap, providing experimentally controlled results on how simple perceptrons trained by gradient descent or by the support vector approach comply with these bounds in practice.

#### References

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#### Paper Citation

#### in Harvard Style

Marques de Sá J., Sereno F. and Alexandre L. (2004). **Neural Network Learning: Testing Bounds on Sample Complexity** . In *Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)* ISBN 972-8865-01-5, pages 196-201. DOI: 10.5220/0002653301960201

#### in Bibtex Style

@conference{pris04,

author={Joaquim Marques de Sá and Fernando Sereno and Luís Alexandre},

title={Neural Network Learning: Testing Bounds on Sample Complexity},

booktitle={Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)},

year={2004},

pages={196-201},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0002653301960201},

isbn={972-8865-01-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)

TI - Neural Network Learning: Testing Bounds on Sample Complexity

SN - 972-8865-01-5

AU - Marques de Sá J.

AU - Sereno F.

AU - Alexandre L.

PY - 2004

SP - 196

EP - 201

DO - 10.5220/0002653301960201