Abstract
| Original language | English |
|---|---|
| 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 | |
|---|---|
| Volume | 16 |
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Maximum-entropy parameter estimation for the k-NN modified value-difference kernel. / Hendrickx, I.H.E.; van den Bosch, A.
Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands. ed. / R. Verbruggen; N. Taatgen; L. Schomaker. Vol. 16 [s.l] : [s.n.], 2004. p. 19-26.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 -