{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:18:16Z","timestamp":1778537896316,"version":"3.51.4"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032159830","type":"print"},{"value":"9783032159847","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-15984-7_13","type":"book-chapter","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:33:31Z","timestamp":1769718811000},"page":"181-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mitigating High Dimensionality and\u00a0Few Training Data with\u00a0a\u00a0Quantum Kernel Learning Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2972-9463","authenticated-orcid":false,"given":"Mauro","family":"Nooblath","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6549-9723","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Bessa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7608-2052","authenticated-orcid":false,"given":"Rosiane","family":"de Freitas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,30]]},"reference":[{"key":"13_CR1","unstructured":"Chollet, F.: Deep Learning with Python. Manning (2017). ISBN: 9781617294433"},{"key":"13_CR2","unstructured":"Combarro, E.F., Gonzalez-Castillo, S., Di Meglio, A.: A practical guide to quantum machine learning and quantum optimization: hands-on approach to modern quantum algorithms. Manning (2023). ISBN: 9781804618301"},{"issue":"7671","key":"13_CR3","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., Lloyd, S.: Quantum machine learning. Nature 549(7671), 195\u2013202 (2017)","journal-title":"Nature"},{"key":"13_CR4","doi-asserted-by":"publisher","unstructured":"Ballabio, D., Grisoni, F., Todeschini, R.: Multivariate comparison of classification performance measures. Chemometr. Intell. Lab. Syst. 174, 33\u201344 (2017). https:\/\/doi.org\/10.1016\/J.CHEMOLAB.2017.12.004","DOI":"10.1016\/J.CHEMOLAB.2017.12.004"},{"key":"13_CR5","doi-asserted-by":"publisher","unstructured":"D\u00fcntsch, I., Gediga, G.: Confusion matrices and rough set data analysis. In: Journal of Physics: Conference Series, vol. 1229 (2019). https:\/\/doi.org\/10.1088\/1742-6596\/1229\/1\/012055","DOI":"10.1088\/1742-6596\/1229\/1\/012055"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Dwivedi, Y.K., Hughes, L., Ismagilova, E., et al.: Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. (2019). https:\/\/doi.org\/10.1016\/J.IJINFOMGT.2019.08.002","DOI":"10.1016\/J.IJINFOMGT.2019.08.002"},{"key":"13_CR7","doi-asserted-by":"publisher","unstructured":"L\u2019Heureux, A., Grolinger, K., ElYamany, H., Capretz, M.A.M.: Machine learning with big data: challenges and approaches. IEEE Access 5, 7776\u20137797 (2017). https:\/\/doi.org\/10.1109\/ACCESS.2017.2696365","DOI":"10.1109\/ACCESS.2017.2696365"},{"key":"13_CR8","doi-asserted-by":"publisher","unstructured":"Bertolini, M., Mezzogori, D., Neroni, M., Zammori, F.: Machine Learning for industrial applications: a comprehensive literature review. Expert Syst. Appl. 175, 114820 (2021). https:\/\/doi.org\/10.1016\/j.eswa.2021.114820","DOI":"10.1016\/j.eswa.2021.114820"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Glassner, A.: An introduction to quantum computing. In: ACM SIGGRAPH 2023 Courses (2023). https:\/\/doi.org\/10.1145\/3587423.3595538","DOI":"10.1145\/3587423.3595538"},{"key":"13_CR10","unstructured":"Nielsen, M.A., Chuang, I.L., Computation, Q., Information, Q.: 10th, Anniversary Cambridge University Press, Cambridge (2010)"},{"key":"13_CR11","unstructured":"IBM Quantum Platform, IBM Quantum Platform (2024). https:\/\/docs.quantum.ibm.com\/. Accessed 24 June 2024"},{"key":"13_CR12","doi-asserted-by":"publisher","unstructured":"Zhang, K., Yu, K., Korepin, V.: Quantum search on noisy intermediate-scale quantum devices. Europhys. Lett. 140 (2022). https:\/\/doi.org\/10.1209\/0295-5075\/ac90e6","DOI":"10.1209\/0295-5075\/ac90e6"},{"key":"13_CR13","doi-asserted-by":"publisher","unstructured":"Gupta, V., Mishra, V., Singhal, P., Kumar, A.: An overview of supervised machine learning algorithm. In: 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), pp. 87\u201392 (2022). https:\/\/doi.org\/10.1109\/SMART55829.2022.10047618","DOI":"10.1109\/SMART55829.2022.10047618"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Schuld, M., Sinayskiy, I., Petruccione, F.: An introduction to quantum machine learning. Contemp. Phys. 56(2), 172\u2013185 (2015)","DOI":"10.1080\/00107514.2014.964942"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Schuld, M., Petruccione, F.: Supervised Learning with Quantum Computers (2018)","DOI":"10.1007\/978-3-319-96424-9"},{"key":"13_CR16","doi-asserted-by":"crossref","unstructured":"Schuld, M.: Supervised quantum machine learning models are kernel methods. arXiv:2101.11020 (2021)","DOI":"10.1007\/978-3-030-83098-4_6"},{"key":"13_CR17","unstructured":"Soontronchai, W.: Kaggle: IIoT Data of Wind Turbine. https:\/\/www.kaggle.com\/datasets\/wasuratme96\/iiot-data-of-wind-turbine. Accessed 2019"},{"key":"13_CR18","doi-asserted-by":"publisher","unstructured":"Kavitha, S., Kaulgud, N.: Quantum machine learning for support vector machine classification. Manning (2022). https:\/\/doi.org\/10.1007\/s12065-022-00756-5","DOI":"10.1007\/s12065-022-00756-5"}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-15984-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:33:34Z","timestamp":1769718814000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-15984-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032159830","9783032159847"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-15984-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fortaleza-CE","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/bracis.sbc.org.br\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}