## Abstract

In this paper we consider the problem of learning nearest-prototype classifiers in any finite distance space; that is, in any finite set equipped with a distance function. An important advantage of a distance space over a metric space is that the triangle inequality need not be satisfied, which makes our results potentially very useful in practice. We consider a family of binary classifiers for learning nearest-prototype classification on distance spaces, building on the concept of large-width learning which we introduced and studied in earlier works. Nearest-prototype is a more general version of the ubiquitous nearest-neighbor classifier: a prototype may or may not be a sample point. One advantage in the approach taken in this paper is that the error bounds depend on a ‘width’ parameter, which can be sample-dependent and thereby yield a tighter bound.

Original language | English |
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Pages (from-to) | 65-79 |

Number of pages | 15 |

Journal | Theoretical Computer Science |

Volume | 738 |

DOIs | |

State | Published - 22 Aug 2018 |

Externally published | Yes |

## Keywords

- Distance space
- LVQ
- Large margin learning
- Metric space
- Nearest-neighbor classification

## ASJC Scopus subject areas

- Theoretical Computer Science
- General Computer Science