### Abstract

The task of any supervised classifier is to assign optimum boundaries in the input space, for the different class membership. This is done using informations from the available set of known samples. This mapping of sample position in the input space to sample class is further used to classify unknown samples. The available set of known sample is generally a finite set. A boundary exactly defined by those finite sample set is usually not the best boundary to classify the new unknown samples. We end up with a overfitted boundary i.e. a overtrained classifier, resulting in poor classification for unknown new samples. We therefore need to smooth the boundary to be able to generalize for the unknown samples. But to what extent? If we smooth the boundary too much, we will not be exploiting all the class informations contained in the known sample set, and the classification result will again be poor. Depending on the number of known samples and the dimension of the actual solution (which, of course, is not known in any of the practical problems), there will be a certain amount of smoothness, which is optimum for generalization. In this paper, we are trying to focus on this problem. We introduce some practical ways to arrive at optimum smoothness, with regards to single hidden layer neural network classifier using radial basis function.

Original language | English |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |

Place of Publication | Piscataway, NJ, United States |

Publisher | Publ by IEEE |

Pages | 2257-2262 |

Number of pages | 6 |

Volume | 3 |

ISBN (Print) | 0780314212, 9780780314214 |

Publication status | Published - 1993 |

Event | Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn Duration: 1993 Oct 25 → 1993 Oct 29 |

### Other

Other | Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) |
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City | Nagoya, Jpn |

Period | 93/10/25 → 93/10/29 |

### Fingerprint

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Proceedings of the International Joint Conference on Neural Networks*(Vol. 3, pp. 2257-2262). Piscataway, NJ, United States: Publ by IEEE.

**Optimization of overtraining and overgeneration.** / Chakraborty, Goutam; Shiratori, Norio; Noguchi, Shoichi.

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

*Proceedings of the International Joint Conference on Neural Networks.*vol. 3, Publ by IEEE, Piscataway, NJ, United States, pp. 2257-2262, Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3), Nagoya, Jpn, 93/10/25.

}

TY - GEN

T1 - Optimization of overtraining and overgeneration

AU - Chakraborty, Goutam

AU - Shiratori, Norio

AU - Noguchi, Shoichi

PY - 1993

Y1 - 1993

N2 - The task of any supervised classifier is to assign optimum boundaries in the input space, for the different class membership. This is done using informations from the available set of known samples. This mapping of sample position in the input space to sample class is further used to classify unknown samples. The available set of known sample is generally a finite set. A boundary exactly defined by those finite sample set is usually not the best boundary to classify the new unknown samples. We end up with a overfitted boundary i.e. a overtrained classifier, resulting in poor classification for unknown new samples. We therefore need to smooth the boundary to be able to generalize for the unknown samples. But to what extent? If we smooth the boundary too much, we will not be exploiting all the class informations contained in the known sample set, and the classification result will again be poor. Depending on the number of known samples and the dimension of the actual solution (which, of course, is not known in any of the practical problems), there will be a certain amount of smoothness, which is optimum for generalization. In this paper, we are trying to focus on this problem. We introduce some practical ways to arrive at optimum smoothness, with regards to single hidden layer neural network classifier using radial basis function.

AB - The task of any supervised classifier is to assign optimum boundaries in the input space, for the different class membership. This is done using informations from the available set of known samples. This mapping of sample position in the input space to sample class is further used to classify unknown samples. The available set of known sample is generally a finite set. A boundary exactly defined by those finite sample set is usually not the best boundary to classify the new unknown samples. We end up with a overfitted boundary i.e. a overtrained classifier, resulting in poor classification for unknown new samples. We therefore need to smooth the boundary to be able to generalize for the unknown samples. But to what extent? If we smooth the boundary too much, we will not be exploiting all the class informations contained in the known sample set, and the classification result will again be poor. Depending on the number of known samples and the dimension of the actual solution (which, of course, is not known in any of the practical problems), there will be a certain amount of smoothness, which is optimum for generalization. In this paper, we are trying to focus on this problem. We introduce some practical ways to arrive at optimum smoothness, with regards to single hidden layer neural network classifier using radial basis function.

UR - http://www.scopus.com/inward/record.url?scp=0027842719&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027842719&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027842719

SN - 0780314212

SN - 9780780314214

VL - 3

SP - 2257

EP - 2262

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Publ by IEEE

CY - Piscataway, NJ, United States

ER -