Christian Igel

Christian Igel

Professor

Member of:

    ORCID: 0000-0003-2868-0856

    Introductory remarks on publicationslist

    This list of pubications is not fully complete.

    For a complete list of older publications as well as papers and software available online please vist my old homepage: https://christian-igel.github.io

    I also maintain a Google Scholar profile: https://scholar.google.dk/citations?user=d-jF4zIAAAAJ


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    1. Published

      Buffer k-d trees: processing massive nearest neighbor queries on GPUs

      Gieseke, Fabian Cristian, Heinermann, J., Oancea, Cosmin Eugen & Igel, Christian, 2014, Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014. 9 p. (JMLR: Workshop and Conference Proceedings, Vol. 32).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    2. Published

      Speedy greedy feature selection: better redshift estimation via massive parallelism

      Gieseke, Fabian Cristian, Polsterer, K. L., Oancea, Cosmin Eugen & Igel, Christian, 2014, ESANN 2014 proceedings: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen, M. (ed.). i6doc.com, p. 87-92 6 p.

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    3. Published

      Bigger buffer k-d trees on multi-many-core systems

      Gieseke, Fabian Cristian, Oancea, Cosmin Eugen, Mahabal, A., Igel, Christian & Heskes, T., 2019, High Performance Computing for Computational Science – VECPAR 2018: 13th International Conference, São Pedro, Brazil, September 17–19, 2018, Revised Selected Papers. Springer, p. 202-214 (Lecture Notes in Computer Science, Vol. 11333).

      Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

    4. Maximum likelihood model selection for 1-norm soft margin SVMs with multiple parameters

      Glasmachers, T. & Igel, Christian, 2010, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 32, 8, p. 1522-1528 7 p.

      Research output: Contribution to journal › Journal article › Research › peer-review

    5. Maximum-gain working set selection for support vector machines

      Glasmachers, T. & Igel, Christian, 2006, In: Journal of Machine Learning Research. 7, p. 1437-1466 30 p.

      Research output: Contribution to journal › Journal article › Research › peer-review

    6. Uncertainty handling in model selection for support vector machines

      Glasmachers, T. & Igel, Christian, 2008, Parallel Problem Solving from Nature – PPSN X: 10th International Conference, Dortmund, Germany, September 13-17, 2008. Proceedings. Rudolph, G., Jansen, T., Beume, N., Lucas, S. & Poloni, C. (eds.). Springer, p. 185-194 10 p. (Lecture notes in computer science, Vol. 5199).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

    7. Second-order SMO improves SVM online and active learning

      Glasmachers, T. & Igel, Christian, 2008, In: Neural Computation. 20, 2, p. 374-382 9 p.

      Research output: Contribution to journal › Journal article › Research › peer-review

    8. Gradient-based adaptation of general gaussian kernels

      Glasmachers, T. & Igel, Christian, 2005, In: Neural Computation. 17, 10, p. 2099-2105 7 p.

      Research output: Contribution to journal › Journal article › Research › peer-review

    9. Published

      Non-linearly increasing resampling in racing algorithms

      Heidrich-Meisner, V. & Igel, Christian, 2011, 19th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2011). Verleysen, M. (ed.). p. 465-470 6 p.

      Research output: Chapter in Book/Report/Conference proceeding › Conference abstract in proceedings › Research › peer-review

    10. Direct policy search: intrinsic vs. extrinsic perturbations

      Heidrich-Meisner, V. & Igel, Christian, 2010, Workshop New Challenges in Neural Computation . Hammer, B. & Villmann, T. (eds.). p. 33-39 7 p. (Machine Learning Reports, Vol. 04/2010).

      Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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