{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:17:32Z","timestamp":1754151452070,"version":"3.41.2"},"reference-count":23,"publisher":"Information Processing Society of Japan","issue":"0","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Information Processing"],"published-print":{"date-parts":[[2025]]},"DOI":"10.2197\/ipsjjip.33.377","type":"journal-article","created":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T22:09:58Z","timestamp":1752530998000},"page":"377-386","source":"Crossref","is-referenced-by-count":0,"title":["Parameter Estimation for Support Vector Machines Using Markov Chain Monte Carlo Simulation"],"prefix":"10.2197","volume":"33","author":[{"given":"Yoshikazu","family":"Sakamaki","sequence":"first","affiliation":[{"name":"Utsunomiya University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1012","reference":[{"key":"1","unstructured":"[1] Alex, S. and Vishwanathan, S.V.N.: <i>Introduction to Machine Learning<\/i>, Cambridge University Press (2008)."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] Alnuaimi, A.F.A.H. and Albaldawi, T.H.K.: An overview of machine learning classification techniques, <i>BIO Web of Conferences<\/i>, Vol.97, DOI: 10.1051\/bioconf\/20249700133 (2024).","DOI":"10.1051\/bioconf\/20249700133"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] Garc\u00eda, N., Mart\u00ednez, T., Ara\u00fajo, F.M. and Ord\u00f3\u00f1ez, G.C.: Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus, <i>Applied Mathematical Modelling<\/i>, Vol.36, No.12, pp.6137-6145 (2012).","DOI":"10.1016\/j.apm.2012.02.016"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Ali, Y., Seyed, A.N. and Reza, A.: The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling, <i>The International Journal of Advanced Manufacturing Technology<\/i>, Vol.105, pp.951-965 (2019).","DOI":"10.1007\/s00170-019-04227-7"},{"key":"5","unstructured":"[5] Vapnik, V. and Lerner, A.: Pattern recognition using generalized portrait method, <i>Automation and Remote Control<\/i>, Vol.24, pp.774-780 (1963)."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] Boser, B., Guyon, I. and Vapnik, V.: A training algorithm for optimal margin classifiers, <i>Proc. 5th Annual Workshop on Computational Learning Theory<\/i>, Pittsburgh (1992).","DOI":"10.1145\/130385.130401"},{"key":"7","unstructured":"[7] Nello, C. and John, S.: <i>An Introduction to Support Vector Machines and Other Kernel-based Learning Methods<\/i>, Cambridge University Press (2000)."},{"key":"8","unstructured":"[8] Ingo, S. and Andreas, C.: <i>Support Vector Machines<\/i> (<i>Information Science and Statistics<\/i>), Springer (2014)."},{"key":"9","unstructured":"[9] Shigeo, A.: <i>Support Vector Machines for Pattern Classification: Advances in Computer Vision and Pattern Recognition<\/i>, Second Edition, Springer (2012)."},{"key":"10","unstructured":"[10] Brandon, H.B.: Support Vector Machines: Data Analysis, <i>Machine Learning and Applications<\/i> (<i>Computer Science, Technology and Applications<\/i>), Nova Science Pub Inc. (2011)."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] Gilks, W.R., Richardson, S. and Spiegelhalter, D.: <i>Markov Chain Monte Carlo in Practice<\/i>, Springer (1995).","DOI":"10.1201\/b14835"},{"key":"12","unstructured":"[12] Faming, L., Chuanhai, L. and Raymond, C.: <i>Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples<\/i>, Wiley (2010)."},{"key":"13","unstructured":"[13] Steve, B., Andrew, G., Galin, J. and Xiao-Li, M.: <i>Handbook of Markov Chain Monte Carlo<\/i>, Chapman and Hall\/CRC (2011)."},{"key":"14","unstructured":"[14] Roweida, M., Jumanah, R. and Malak, A.A.: Machine Learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results, <i>Conference: 2020 11th International Conference on Information and Communication Systems<\/i> (<i>ICICS<\/i>) (2020), available from &lt;https:\/\/www.researchgate.net\/publication\/340978368&gt;"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] Sun, Y., Wong, A.K. and Kamel, M.S.: Classification of imbalanced data: A review, <i>International Journal of Pattern Recognition and Artificial Intelligence<\/i>, Vol.23, No.4, pp.687-719 (2009).","DOI":"10.1142\/S0218001409007326"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] He, H. and Garcia, E.A.: Learning from imbalanced data, <i>IEEE Trans. Knowledge and Data Engineering<\/i>, Vol.21, No.9, pp.1263-1284 (2009).","DOI":"10.1109\/TKDE.2008.239"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Tomek, I.: A generalization of the k-nn rule, <i>IEEE Trans. Systems, Man, and Cybernetics<\/i>, No.2, pp.121-126 (1976).","DOI":"10.1109\/TSMC.1976.5409182"},{"key":"18","unstructured":"[18] Lema\u0131tre, G., Nogueira, F. and Aridas, C.K.: Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning, <i>The Journal of Machine Learning Research<\/i>, Vol.18, No.1, pp.559-563 (2017)."},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P.: SMOTE: Synthetic minority over-sampling technique, <i>Journal of Artificial Intelligence Research<\/i>, Vol.16, pp.321-357 (2002).","DOI":"10.1613\/jair.953"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] Han, H., Wang, W.Y. and Mao, B.H.: Borderline-smote: A new oversampling method in imbalanced data sets learning, <i>International Conference on Intelligent Computing<\/i>, pp.878-887, Springer (2005).","DOI":"10.1007\/11538059_91"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] Bunkhumpornpat, C., Sinapiromsaran, K. and Lursinsap, C.: Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem, <i>Pacific-Asia Conference on Knowledge Discovery and Data Mining<\/i>, pp.475-482, Springer (2009).","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] Ramaprasad, P., Roma, R. and Amit, K.: Effect of data-augmentation on fine-tuned CNN model performance, <i>IAES International Journal of Artificial Intelligence<\/i>, Vol.10, No.1, pp.84-92 (2021), available from &lt;http:\/\/ijai.iaescore.com&gt;","DOI":"10.11591\/ijai.v10.i1.pp84-92"},{"key":"23","doi-asserted-by":"crossref","unstructured":"[23] Jude, C.O.: A comparative study of several classification metrics and their performances on data, <i>World Journal of Advanced Engineering Technology and Sciences<\/i>, Vol.8, No.1, pp.308-314 (2023).","DOI":"10.30574\/wjaets.2023.8.1.0054"}],"container-title":["Journal of Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/ipsjjip\/33\/0\/33_377\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T03:34:39Z","timestamp":1752896079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/ipsjjip\/33\/0\/33_377\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":23,"journal-issue":{"issue":"0","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.2197\/ipsjjip.33.377","relation":{},"ISSN":["1882-6652"],"issn-type":[{"type":"electronic","value":"1882-6652"}],"subject":[],"published":{"date-parts":[[2025]]}}}