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Abstract
We introduce an extension of the modified valuedifference kernel of $k$nn by replacing the kernel's default class distribution matrix with the matrix produced by the maximumentropy learning algorithm. This hybrid algorithm is tested on fifteen machine learning benchmark tasks, comparing the hybrid to standard $k$nn classification and maximumentropybased classification. Results show that the hybrid typically outperforms the lowerscoring 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 

Title of host publication  Proceedings of the 16th BelgianDutch Conference on Artificial Intelligence (BNAIC 2004), 2122 october 2004, Groningen, The Netherlands 
Editors  R. Verbruggen, N. Taatgen, L. Schomaker 
Place of Publication  [s.l] 
Publisher  [s.n.] 
Pages  1926 
Number of pages  8 
Volume  16 
Publication status  Published  2004 
Publication series
Name  

Volume  16 
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Dive into the research topics of 'Maximumentropy parameter estimation for the kNN modified valuedifference kernel'. Together they form a unique fingerprint.Projects
 2 Finished

Algorithm development for memory models of language
Hendrickx, I. H. E., Daelemans, W. M. P. & van den Bosch, A.
1/09/01 → 1/09/05
Project: Research project
