### Abstract

Original language | English |
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Title of host publication | Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands |

Editors | R. Verbruggen, N. Taatgen, L. Schomaker |

Place of Publication | [s.l] |

Publisher | [s.n.] |

Pages | 19-26 |

Number of pages | 8 |

Volume | 16 |

Publication status | Published - 2004 |

### Publication series

Name | |
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Volume | 16 |

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### Cite this

*Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands*(Vol. 16, pp. 19-26). [s.l]: [s.n.].

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*Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands.*vol. 16, [s.n.], [s.l], pp. 19-26.

**Maximum-entropy parameter estimation for the k-NN modified value-difference kernel.** / Hendrickx, I.H.E.; van den Bosch, A.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Maximum-entropy parameter estimation for the k-NN modified value-difference kernel

AU - Hendrickx, I.H.E.

AU - van den Bosch, A.

N1 - Pagination: 8

PY - 2004

Y1 - 2004

N2 - We introduce an extension of the modified value-difference kernel of $k$-nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid to standard $k$-nn classification and maximum-entropy-based classification. Results show that the hybrid typically outperforms the lower-scoring of the two other algorithms, often significantly; in a majority of cases the hybrid yields the highest accuracy of the three algorithms. Error analysis indicates that the hybrid's errors overlap more with $k$-nn than with maximum entropy modeling

AB - We introduce an extension of the modified value-difference kernel of $k$-nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximum-entropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid to standard $k$-nn classification and maximum-entropy-based classification. Results show that the hybrid typically outperforms the lower-scoring of the two other algorithms, often significantly; in a majority of cases the hybrid yields the highest accuracy of the three algorithms. Error analysis indicates that the hybrid's errors overlap more with $k$-nn than with maximum entropy modeling

M3 - Conference contribution

VL - 16

SP - 19

EP - 26

BT - Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands

A2 - Verbruggen, R.

A2 - Taatgen, N.

A2 - Schomaker, L.

PB - [s.n.]

CY - [s.l]

ER -