[{"volume":34,"user_id":"102979","_id":"48877","page":"977–991","status":"public","citation":{"mla":"Casalicchio, Giuseppe, et al. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i>, vol. 34, no. 3, 2019, pp. 977–991, doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","bibtex":"@article{Casalicchio_Bossek_Lang_Kirchhoff_Kerschke_Hofner_Seibold_Vanschoren_Bischl_2019, title={OpenML: An R Package to Connect to the Machine Learning Platform OpenML}, volume={34}, DOI={<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>}, number={3}, journal={Computational Statistics}, author={Casalicchio, Giuseppe and Bossek, Jakob and Lang, Michel and Kirchhoff, Dominik and Kerschke, Pascal and Hofner, Benjamin and Seibold, Heidi and Vanschoren, Joaquin and Bischl, Bernd}, year={2019}, pages={977–991} }","ama":"Casalicchio G, Bossek J, Lang M, et al. OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>. 2019;34(3):977–991. doi:<a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>","ieee":"G. Casalicchio <i>et al.</i>, “OpenML: An R Package to Connect to the Machine Learning Platform OpenML,” <i>Computational Statistics</i>, vol. 34, no. 3, pp. 977–991, 2019, doi: <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">10.1007/s00180-017-0742-2</a>.","apa":"Casalicchio, G., Bossek, J., Lang, M., Kirchhoff, D., Kerschke, P., Hofner, B., Seibold, H., Vanschoren, J., &#38; Bischl, B. (2019). OpenML: An R Package to Connect to the Machine Learning Platform OpenML. <i>Computational Statistics</i>, <i>34</i>(3), 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>","chicago":"Casalicchio, Giuseppe, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, and Bernd Bischl. “OpenML: An R Package to Connect to the Machine Learning Platform OpenML.” <i>Computational Statistics</i> 34, no. 3 (2019): 977–991. <a href=\"https://doi.org/10.1007/s00180-017-0742-2\">https://doi.org/10.1007/s00180-017-0742-2</a>.","short":"G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, B. Bischl, Computational Statistics 34 (2019) 977–991."},"doi":"10.1007/s00180-017-0742-2","language":[{"iso":"eng"}],"intvolume":"        34","date_updated":"2023-12-13T10:51:17Z","author":[{"full_name":"Casalicchio, Giuseppe","first_name":"Giuseppe","last_name":"Casalicchio"},{"last_name":"Bossek","orcid":"0000-0002-4121-4668","first_name":"Jakob","full_name":"Bossek, Jakob","id":"102979"},{"first_name":"Michel","last_name":"Lang","full_name":"Lang, Michel"},{"last_name":"Kirchhoff","first_name":"Dominik","full_name":"Kirchhoff, Dominik"},{"full_name":"Kerschke, Pascal","last_name":"Kerschke","first_name":"Pascal"},{"full_name":"Hofner, Benjamin","last_name":"Hofner","first_name":"Benjamin"},{"full_name":"Seibold, Heidi","first_name":"Heidi","last_name":"Seibold"},{"full_name":"Vanschoren, Joaquin","last_name":"Vanschoren","first_name":"Joaquin"},{"full_name":"Bischl, Bernd","first_name":"Bernd","last_name":"Bischl"}],"publication_identifier":{"issn":["0943-4062"]},"title":"OpenML: An R Package to Connect to the Machine Learning Platform OpenML","year":"2019","department":[{"_id":"819"}],"keyword":["Databases","Machine learning","R","Reproducible research"],"type":"journal_article","date_created":"2023-11-14T15:58:57Z","abstract":[{"lang":"eng","text":"OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination with the machine learning R package mlr (Bischl et al. J Mach Learn Res 17(170):1—5, 2016). We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks. Furthermore, we also show how to upload results of experiments, share them with others and download results from other users. Beyond ensuring reproducibility of results, the OpenML platform automates much of the drudge work, speeds up research, facilitates collaboration and increases the users’ visibility online."}],"issue":"3","publication":"Computational Statistics"},{"intvolume":"        17","date_updated":"2022-01-06T06:51:12Z","author":[{"full_name":"Windmann, Stefan","first_name":"Stefan","last_name":"Windmann"},{"full_name":"Haeb-Umbach, Reinhold","first_name":"Reinhold","last_name":"Haeb-Umbach","id":"242"}],"year":"2009","title":"Approaches to Iterative Speech Feature Enhancement and Recognition","doi":"10.1109/TASL.2009.2014894","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-1.pdf"}],"abstract":[{"text":"In automatic speech recognition, hidden Markov models (HMMs) are commonly used for speech decoding, while switching linear dynamic models (SLDMs) can be employed for a preceding model-based speech feature enhancement. In this paper, these model types are combined in order to obtain a novel iterative speech feature enhancement and recognition architecture. It is shown that speech feature enhancement with SLDMs can be improved by feeding back information from the HMM to the enhancement stage. Two different feedback structures are derived. In the first, the posteriors of the HMM states are used to control the model probabilities of the SLDMs, while in the second they are employed to directly influence the estimate of the speech feature distribution. Both approaches lead to improvements in recognition accuracy both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature enhancement with SLDMs. It is also shown that a combination with uncertainty decoding further enhances performance.","lang":"eng"}],"publication":"IEEE Transactions on Audio, Speech, and Language Processing","issue":"5","department":[{"_id":"54"}],"type":"journal_article","keyword":["AURORA2 databases","AURORA4 databases","automatic speech recognition","feedback structures","hidden Markov models","HMM","iterative methods","iterative speech feature enhancement","model probabilities","speech decoding","speech enhancement","speech feature distribution","speech recognition","switching linear dynamic models"],"date_created":"2019-07-12T05:31:08Z","status":"public","volume":17,"user_id":"44006","_id":"11937","page":"974-984","citation":{"bibtex":"@article{Windmann_Haeb-Umbach_2009, title={Approaches to Iterative Speech Feature Enhancement and Recognition}, volume={17}, DOI={<a href=\"https://doi.org/10.1109/TASL.2009.2014894\">10.1109/TASL.2009.2014894</a>}, number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={974–984} }","ama":"Windmann S, Haeb-Umbach R. Approaches to Iterative Speech Feature Enhancement and Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>. 2009;17(5):974-984. doi:<a href=\"https://doi.org/10.1109/TASL.2009.2014894\">10.1109/TASL.2009.2014894</a>","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 17, no. 5, 2009, pp. 974–84, doi:<a href=\"https://doi.org/10.1109/TASL.2009.2014894\">10.1109/TASL.2009.2014894</a>.","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i> 17, no. 5 (2009): 974–84. <a href=\"https://doi.org/10.1109/TASL.2009.2014894\">https://doi.org/10.1109/TASL.2009.2014894</a>.","short":"S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language Processing 17 (2009) 974–984.","ieee":"S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement and Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, vol. 17, no. 5, pp. 974–984, 2009.","apa":"Windmann, S., &#38; Haeb-Umbach, R. (2009). Approaches to Iterative Speech Feature Enhancement and Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>, <i>17</i>(5), 974–984. <a href=\"https://doi.org/10.1109/TASL.2009.2014894\">https://doi.org/10.1109/TASL.2009.2014894</a>"},"oa":"1"}]