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

    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 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]
    Publisher[s.n.]
    Pages19-26
    Number of pages8
    Volume16
    Publication statusPublished - 2004

    Publication series

    Name
    Volume16

    Fingerprint

    Parameter estimation
    Entropy
    Error analysis
    Learning algorithms
    Learning systems

    Cite this

    Hendrickx, I. H. E., & van den Bosch, A. (2004). Maximum-entropy parameter estimation for the k-NN modified value-difference kernel. In R. Verbruggen, N. Taatgen, & L. Schomaker (Eds.), 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.].
    Hendrickx, I.H.E. ; van den Bosch, A. / Maximum-entropy parameter estimation for the k-NN modified value-difference kernel. Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands. editor / R. Verbruggen ; N. Taatgen ; L. Schomaker. Vol. 16 [s.l] : [s.n.], 2004. pp. 19-26
    @inproceedings{fed1eb75669f4bb88c8c4cd2d21a6d75,
    title = "Maximum-entropy parameter estimation for the k-NN modified value-difference kernel",
    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",
    author = "I.H.E. Hendrickx and {van den Bosch}, A.",
    note = "Pagination: 8",
    year = "2004",
    language = "English",
    volume = "16",
    publisher = "[s.n.]",
    pages = "19--26",
    editor = "R. Verbruggen and N. Taatgen and L. Schomaker",
    booktitle = "Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands",

    }

    Hendrickx, IHE & van den Bosch, A 2004, Maximum-entropy parameter estimation for the k-NN modified value-difference kernel. in R Verbruggen, N Taatgen & L Schomaker (eds), 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.

    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 proceedingConference contributionScientificpeer-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 -

    Hendrickx IHE, van den Bosch A. Maximum-entropy parameter estimation for the k-NN modified value-difference kernel. In Verbruggen R, Taatgen N, Schomaker L, editors, Proceedings of the 16th Belgian-Dutch Conference on Artificial Intelligence (BNAIC 2004), 21-22 october 2004, Groningen, The Netherlands. Vol. 16. [s.l]: [s.n.]. 2004. p. 19-26