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Abstract
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
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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Dive into the research topics of 'Maximum-entropy parameter estimation for the k-NN modified value-difference kernel'. Together they form a unique fingerprint.Projects
- 2 Finished
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Algorithm development for memory models of language
Hendrickx, I. H. E. (Researcher), Daelemans, W. M. P. (Tutor) & van den Bosch, A. (Coach)
1/09/01 → 1/09/05
Project: Research project
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