{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T02:42:50Z","timestamp":1648608170958},"reference-count":13,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2022,1,1]]},"DOI":"10.1587\/transinf.2021edp7156","type":"journal-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T22:24:52Z","timestamp":1640989492000},"page":"116-122","source":"Crossref","is-referenced-by-count":0,"title":["Kernel-Based Regressors Equivalent to Stochastic Affine Estimators"],"prefix":"10.1587","volume":"E105.D","author":[{"given":"Akira","family":"TANAKA","sequence":"first","affiliation":[{"name":"Division of Computer Science and Information Technology, Faculty of Information Science and Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masanari","family":"NAKAMURA","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Information Technology, Faculty of Information Science and Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hideyuki","family":"IMAI","sequence":"additional","affiliation":[{"name":"Division of Computer Science and Information Technology, Faculty of Information Science and Technology, Hokkaido University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[3] K.-R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf, \u201cAn introduction to kernel-based learning algorithms,\u201d IEEE Trans. Neural Netw., vol.12, no.2, pp.181-201, 2001. 10.1109\/72.914517","DOI":"10.1109\/72.914517"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[4] N. Aronszajn, \u201cTheory of reproducing kernels,\u201d Trans. American Mathematical Society, vol.68, no.3, pp.337-404, 1950. 10.1090\/s0002-9947-1950-0051437-7","DOI":"10.1090\/S0002-9947-1950-0051437-7"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[5] A. Tanaka and H. Imai, \u201cKernel ridge regression with autocorrelation prior: Optimal model and cross-validation,\u201d 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP2020), pp.3867-3871, 2020. 10.1109\/icassp40776.2020.9053423","DOI":"10.1109\/ICASSP40776.2020.9053423"},{"key":"4","unstructured":"[6] T. Kailath, A.H. Sayed, and B. Hassibi, Linear Estimation, Prentice-Hall, Upper Saddle River, NJ, 2000."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[7] C.E. Rasmussen, \u201cGaussian processes in machine learning,\u201d Summer School on Machine Learning, Lecture Notes in Computer Science, vol.3176, pp.63-71, Springer 2004. 10.1007\/978-3-540-28650-9_4","DOI":"10.1007\/978-3-540-28650-9_4"},{"key":"6","unstructured":"[9] K.P. Murphy, Machine learning: A probabilistic perspective, MIT Press, 2012."},{"key":"7","unstructured":"[10] M. Kanagawa, P. Hennig, D. Sejdinovic, and B.K. Sriperumbudur, \u201cGaussian process and kernel methods: A review on connections and equivalences,\u201d arXiv:1807.02582 [stat.ML], 2018."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[11] A. Tanaka, H. Imai, and M. Miyakoshi, \u201cKernel-induced sampling theorem,\u201d IEEE Trans. Signal Process., vol.58, no.7, pp.3569-3577, 2010. 10.1109\/tsp.2010.2046637","DOI":"10.1109\/TSP.2010.2046637"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[12] J. Mercer, \u201cFunctions of positive and negative type, and their connection with the theory of integral equations,\u201d Philosophical Trans. Royal Society A, vol.209, no.441-458, pp.415-446, 1909. 10.1098\/rsta.1909.0016","DOI":"10.1098\/rsta.1909.0016"},{"key":"10","unstructured":"[13] S. Saitoh, Integral Transforms, Reproducing Kernels and Their Applications, Addison Wesley Longman Ltd, UK, 1997."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[14] B. Sch\u00f6lkopf, R. Herbrich, and A. Smola, \u201cA generalized representer theorem,\u201d Computational Learning Theory, Lecture Notes in Computer Science, vol.2111, pp.416-426, Springer, 2001. 10.1007\/3-540-44581-1_27","DOI":"10.1007\/3-540-44581-1_27"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[15] R. Kong and B. Zhang, \u201cAutocorrelation kernel functions for support vector machines,\u201d The 3rd International Conference on Natural Computation, 2007. 10.1109\/icnc.2007.276","DOI":"10.1109\/ICNC.2007.276"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[16] Y. Saito and A. Tanaka, \u201cOptimal kernel in kernel regression problems with autocorrelation prior,\u201d 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp.969-972, IEEE, 2017. 10.1109\/apsipa.2017.8282163","DOI":"10.1109\/APSIPA.2017.8282163"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/1\/E105.D_2021EDP7156\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T04:17:54Z","timestamp":1641010674000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E105.D\/1\/E105.D_2021EDP7156\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":13,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2021edp7156","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]},"article-number":"2021EDP7156"}}