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

I.H.E. Hendrickx, A. van den Bosch

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


    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 languageEnglish
    Title of host publicationProceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands
    EditorsR. Verbruggen, N. Taatgen, L. Schomaker
    Place of Publication[s.l]
    Number of pages8
    Publication statusPublished - 2004

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      Project: Research project

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