### Abstract

The authors concentrate on estimating how many hidden units are needed for large nets. The design method is adapted from a more rigorous one developed for smaller nets where all possible input/output relationships are presented in the training vectors. It provides a 'ballpark' estimate of the minimum number of hidden units required by a two-layer perceptron for a provided training subset of the possible input/output pairs. Typically, the number of hidden units is slightly overestimated in this approach. To obtain an estimate of the number of hidden units for a fully connected net with n output units, it is necessary to obtain an estimate of the number of conflicts contained in the individual binary responses that must be learned by each ouput unit. The estimate produced is data dependent, as the number of conflicts for an output unit depends on the specific responses of the output unit to the input vectors contained in the training set.

Original language | English (US) |
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Title of host publication | IJCNN Int Jt Conf Neural Network |

Editors | Anon |

Place of Publication | Piscataway, NJ, United States |

Publisher | Publ by IEEE |

Pages | 677-681 |

Number of pages | 5 |

State | Published - 1989 |

Event | IJCNN International Joint Conference on Neural Networks - Washington, DC, USA Duration: Jun 18 1989 → Jun 22 1989 |

### Other

Other | IJCNN International Joint Conference on Neural Networks |
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City | Washington, DC, USA |

Period | 6/18/89 → 6/22/89 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*IJCNN Int Jt Conf Neural Network*(pp. 677-681). Piscataway, NJ, United States: Publ by IEEE.

**Estimating hidden unit number for two-layer perceptrons.** / Gutierrez, Mario; Wang, Jennifer; Grondin, Robert.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*IJCNN Int Jt Conf Neural Network.*Publ by IEEE, Piscataway, NJ, United States, pp. 677-681, IJCNN International Joint Conference on Neural Networks, Washington, DC, USA, 6/18/89.

}

TY - GEN

T1 - Estimating hidden unit number for two-layer perceptrons

AU - Gutierrez, Mario

AU - Wang, Jennifer

AU - Grondin, Robert

PY - 1989

Y1 - 1989

N2 - The authors concentrate on estimating how many hidden units are needed for large nets. The design method is adapted from a more rigorous one developed for smaller nets where all possible input/output relationships are presented in the training vectors. It provides a 'ballpark' estimate of the minimum number of hidden units required by a two-layer perceptron for a provided training subset of the possible input/output pairs. Typically, the number of hidden units is slightly overestimated in this approach. To obtain an estimate of the number of hidden units for a fully connected net with n output units, it is necessary to obtain an estimate of the number of conflicts contained in the individual binary responses that must be learned by each ouput unit. The estimate produced is data dependent, as the number of conflicts for an output unit depends on the specific responses of the output unit to the input vectors contained in the training set.

AB - The authors concentrate on estimating how many hidden units are needed for large nets. The design method is adapted from a more rigorous one developed for smaller nets where all possible input/output relationships are presented in the training vectors. It provides a 'ballpark' estimate of the minimum number of hidden units required by a two-layer perceptron for a provided training subset of the possible input/output pairs. Typically, the number of hidden units is slightly overestimated in this approach. To obtain an estimate of the number of hidden units for a fully connected net with n output units, it is necessary to obtain an estimate of the number of conflicts contained in the individual binary responses that must be learned by each ouput unit. The estimate produced is data dependent, as the number of conflicts for an output unit depends on the specific responses of the output unit to the input vectors contained in the training set.

UR - http://www.scopus.com/inward/record.url?scp=0024903275&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024903275&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0024903275

SP - 677

EP - 681

BT - IJCNN Int Jt Conf Neural Network

A2 - Anon, null

PB - Publ by IEEE

CY - Piscataway, NJ, United States

ER -