### Abstract

Incremental learning algorithms are presented for Nearest Prototype (NP) classifiers. Fuzzification of the 1-NP and k-NP classification rules provides an explicit analytical expression of the membership of data to categories. This expression enables formulation of the prototype placement problem as mathematical programming which can be solved by using a gradient descent algorithm. In addition to the learning algorithm, analog electronic circuits are configured which implement 1-NP and k-NP classifiers.

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

Publisher | Publ by IEEE |

Pages | 585-588 |

Number of pages | 4 |

Volume | 1 |

ISBN (Print) | 0780314212, 9780780314214 |

Publication status | Published - 1993 |

Externally published | Yes |

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

### Other

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

Period | 10/25/93 → 10/29/93 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Engineering(all)

### Cite this

*Proceedings of the International Joint Conference on Neural Networks*(Vol. 1, pp. 585-588). Publ by IEEE.

**Learning algorithm for nearest-prototype classifiers.** / Urahama, Kiichi; Nagao, Takeshi.

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

*Proceedings of the International Joint Conference on Neural Networks.*vol. 1, Publ by IEEE, pp. 585-588, Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3), Nagoya, Jpn, 10/25/93.

}

TY - GEN

T1 - Learning algorithm for nearest-prototype classifiers

AU - Urahama, Kiichi

AU - Nagao, Takeshi

PY - 1993

Y1 - 1993

N2 - Incremental learning algorithms are presented for Nearest Prototype (NP) classifiers. Fuzzification of the 1-NP and k-NP classification rules provides an explicit analytical expression of the membership of data to categories. This expression enables formulation of the prototype placement problem as mathematical programming which can be solved by using a gradient descent algorithm. In addition to the learning algorithm, analog electronic circuits are configured which implement 1-NP and k-NP classifiers.

AB - Incremental learning algorithms are presented for Nearest Prototype (NP) classifiers. Fuzzification of the 1-NP and k-NP classification rules provides an explicit analytical expression of the membership of data to categories. This expression enables formulation of the prototype placement problem as mathematical programming which can be solved by using a gradient descent algorithm. In addition to the learning algorithm, analog electronic circuits are configured which implement 1-NP and k-NP classifiers.

UR - http://www.scopus.com/inward/record.url?scp=0027855188&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0027855188&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027855188

SN - 0780314212

SN - 9780780314214

VL - 1

SP - 585

EP - 588

BT - Proceedings of the International Joint Conference on Neural Networks

PB - Publ by IEEE

ER -