{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:15:06Z","timestamp":1762298106749,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319076911"},{"type":"electronic","value":"9783319076928"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-07692-8_26","type":"book-chapter","created":{"date-parts":[[2014,5,29]],"date-time":"2014-05-29T16:19:57Z","timestamp":1401380397000},"page":"273-281","source":"Crossref","is-referenced-by-count":9,"title":["Kernel Functions for the Support Vector Machine: Comparing Performances on Crude Oil Price Data"],"prefix":"10.1007","author":[{"given":"Haruna","family":"Chiroma","sequence":"first","affiliation":[]},{"given":"Sameem","family":"Abdulkareem","sequence":"additional","affiliation":[]},{"given":"Adamu I.","family":"Abubakar","sequence":"additional","affiliation":[]},{"given":"Tutut","family":"Herawan","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"26_CR1","first-page":"299","volume":"2","author":"M.G. Genton","year":"2001","unstructured":"Genton, M.G.: Classes of Kernels for Machine Learning: A Statistics Perspective. J. Mach. Learn. Res.\u00a02, 299\u2013312 (2001)","journal-title":"J. Mach. Learn. Res."},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/3-540-33880-2_26","volume":"23","author":"D. Ben-Shimon","year":"2006","unstructured":"Ben-Shimon, D., Shmilovici, A.: Kernels for the Relevance Vector Machine: An empirical Study. Adv. Web Intel. Data Min.\u00a023, 253\u2013263 (2006)","journal-title":"Adv. Web Intel. Data Min."},{"key":"26_CR3","unstructured":"Dem\u0161ar J.: Statistical Comparisons of Classifiers over Multiple Data Sets. J. Mach. Learn. Res.\u00a07, 1\u201330 (2006)"},{"key":"26_CR4","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques (Data Management Systems)","author":"I.H. Witten","year":"2005","unstructured":"Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques (Data Management Systems). Morgan Kaufmann, San Mateo (2005)"},{"key":"26_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389","volume-title":"An introduction to support vector machines and other kernel-based learning methods","author":"N. Cristianini","year":"2000","unstructured":"Cristianini, N., Taylor, J.S.: An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, New York (2000)"},{"key":"26_CR6","first-page":"211","volume":"1","author":"M.E. Tipping","year":"2001","unstructured":"Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res.\u00a01, 211\u2013244 (2001)","journal-title":"J. Mach. Learn. Res."},{"key":"26_CR7","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.energy.2012.07.055","volume":"46","author":"K. He","year":"2012","unstructured":"He, K., Yu, L., Lai, K.K.: Crude oil price analysis and forecasting using wavelet decomposed ensemble Model. Energy\u00a046, 564\u2013574 (2012)","journal-title":"Energy"},{"key":"26_CR8","doi-asserted-by":"publisher","first-page":"4267","DOI":"10.1016\/j.enpol.2009.05.026","volume":"37","author":"A. Charles","year":"2009","unstructured":"Charles, A., Darne\u00b4, O.: The efficiency of the crude oil markets: Evidence from variance ratio tests. Energ. Policy\u00a037, 4267\u20134272 (2009)","journal-title":"Energ. Policy"},{"issue":"4","key":"26_CR9","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1109\/LGRS.2007.903069","volume":"4","author":"B. Demir","year":"2007","unstructured":"Demir, B., Ert\u00fcrk, S.: Hyperspectral image classification using relevance vector machines. IEEE Geoscience Remotes\u00a04(4), 586\u2013590 (2007)","journal-title":"IEEE Geoscience Remotes"},{"key":"26_CR10","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-981-4585-18-7_23","volume-title":"Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng 2013)","author":"H. Chiroma","year":"2014","unstructured":"Chiroma, H., Abdul-Kareem, S., Abubakar, A., Akram, M., Zeki, A.M., Usman, M.J.: Orthogonal Wavelet Support Vector Machine for Predicting Crude Oil Prices. In: Herawan, T., Deris, M.M., Abawajy, J. (eds.) Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng 2013), vol.\u00a0285, pp. 193\u2013201. Springer, Singapore (2014)"}],"container-title":["Advances in Intelligent Systems and Computing","Recent Advances on Soft Computing and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-07692-8_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T04:28:17Z","timestamp":1676867297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-07692-8_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319076911","9783319076928"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-07692-8_26","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2014]]}}}