### 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 |

### All Science Journal Classification (ASJC) codes

- Engineering(all)

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## Cite this

Urahama, K., & Nagao, T. (1993). Learning algorithm for nearest-prototype classifiers. In

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