{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T03:29:26Z","timestamp":1762658966553},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2007,11,17]],"date-time":"2007-11-17T00:00:00Z","timestamp":1195257600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2007,12,6]]},"DOI":"10.1007\/s10994-007-5035-5","type":"journal-article","created":{"date-parts":[[2007,11,16]],"date-time":"2007-11-16T15:06:58Z","timestamp":1195225618000},"page":"89-118","source":"Crossref","is-referenced-by-count":63,"title":["Incorporating prior knowledge in support vector regression"],"prefix":"10.1007","volume":"70","author":[{"given":"Fabien","family":"Lauer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00e9rard","family":"Bloch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2007,11,17]]},"reference":[{"key":"5035_CR1","unstructured":"Andrews, R., & Geva, S. (1999). On the effects of initializing a neural network with prior knowledge. In Proceedings of the international conference on neural information processing, Perth, Western Australia (pp.\u00a0251\u2013256)."},{"key":"5035_CR2","first-page":"307","volume-title":"Advances in kernel methods: support vector learning","author":"K. P. Bennett","year":"1999","unstructured":"Bennett, K. P. (1999). Combining support vector and mathematical programming methods for classification. In B. Sch\u00f6lkopf, C. J. Burges, & A. J. Smola (Eds.), Advances in kernel methods: support vector learning (pp. 307\u2013326). Cambridge: MIT Press."},{"key":"5035_CR3","unstructured":"Bloch, G., Lauer, F., Colin, G., & Chamaillard, Y. (2007). Combining experimental data and physical simulation models in support vector learning. In Proceedings of the 10th international conference on engineering applications of neural networks (pp.\u00a0284\u2013295), Thessaloniki, Greece."},{"key":"5035_CR4","doi-asserted-by":"crossref","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., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press."},{"key":"5035_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/A:1018946025316","volume":"13","author":"T. Evgeniou","year":"2000","unstructured":"Evgeniou, T., Pontil, M., & Poggio, T. (2000). Regularization networks and support vector machines. Advances in Computational Mathematics, 13, 1\u201350.","journal-title":"Advances in Computational Mathematics"},{"key":"5035_CR6","first-page":"521","volume-title":"NIPS","author":"G. Fung","year":"2002","unstructured":"Fung, G., Mangasarian, O. L., & Shavlik, J. W. (2002). Knowledge-based support vector machine classifiers. In S. Becker, S. Thrun, & K. Obermayer (Eds.), NIPS (pp. 521\u2013528). Cambridge: MIT Press."},{"key":"5035_CR7","series-title":"Lecture notes in computer science","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1007\/978-3-540-45167-9_9","volume-title":"COLT","author":"G. Fung","year":"2003","unstructured":"Fung, G., Mangasarian, O. L., & Shavlik, J. W. (2003). Knowledge-based nonlinear kernel classifiers. In Sch\u00f6lkopf, B. & Warmuth, M. K. (Eds.), Lecture notes in computer science : Vol.\u00a02777. COLT (pp.\u00a0102\u2013113). Berlin: Springer."},{"issue":"4","key":"5035_CR8","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1243\/14680874JER00407","volume":"8","author":"P. Giansetti","year":"2007","unstructured":"Giansetti, P., Colin, G., Higelin, P., & Chamaillard, Y. (2007). Residual gas fraction measurement and computation. International Journal of Engine Research, 8(4), 347\u2013364.","journal-title":"International Journal of Engine Research"},{"key":"5035_CR9","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21606-5","volume-title":"The elements of statistical learning: data mining, inference, and prediction","author":"T. Hastie","year":"2001","unstructured":"Hastie, T., Tibshirani, R., & Friedman, J. et al. (2001). The elements of statistical learning: data mining, inference, and prediction. Berlin: Springer."},{"key":"5035_CR10","unstructured":"Imagine (2006). Amesim web site www.amesim.com ."},{"key":"5035_CR11","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4615-0907-3","volume-title":"Learning to classify text using support vector machines: methods, theory and algorithms","author":"T. Joachims","year":"2002","unstructured":"Joachims, T. (2002). Learning to classify text using support vector machines: methods, theory and algorithms. Dordrecht: Kluwer Academic."},{"issue":"3","key":"5035_CR12","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/0005-1098(95)00146-8","volume":"32","author":"T. Johansen","year":"1996","unstructured":"Johansen, T. (1996). Identification of non-linear systems using empirical data and prior knowledge-an optimization approach. Automatica, 32(3), 337\u2013356.","journal-title":"Automatica"},{"key":"5035_CR13","doi-asserted-by":"crossref","unstructured":"Lauer, F., & Bloch, G. (2007, to appear). Incorporating prior knowledge in support vector machines for classification: a review. Neurocomputing.","DOI":"10.1016\/j.neucom.2007.04.010"},{"key":"5035_CR14","series-title":"Classics in applied mathematics","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611971217","volume-title":"Solving least squares problems","author":"C. L. Lawson","year":"1995","unstructured":"Lawson, C. L., & Hanson, R. J. (1995). Solving least squares problems, Classics in applied mathematics. Philadelphia: SIAM."},{"key":"5035_CR15","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/LSP.2004.840841","volume":"12","author":"M. L\u00e1zaro","year":"2005","unstructured":"L\u00e1zaro, M., P\u00e9rez-Cruz, F., & Art\u00e9s-Rodriguez, A. (2005a). Learning a function and its derivative forcing the support vector expansion. IEEE Signal Processing Letters, 12, 194\u2013197.","journal-title":"IEEE Signal Processing Letters"},{"key":"5035_CR16","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.neucom.2005.02.013","volume":"69","author":"M. L\u00e1zaro","year":"2005","unstructured":"L\u00e1zaro, M., Santamaria, I., P\u00e9rez-Cruz, F., & Art\u00e9s-Rodriguez, A. (2005b). Support vector regression for the simultaneous learning of a multivariate function and its derivatives. Neurocomputing, 69, 42\u201361.","journal-title":"Neurocomputing"},{"key":"5035_CR17","unstructured":"Maclin, R., Shavlik, J., Torrey, L., Walker, T., & Wild, E. (2005). Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression. In Proceedings of the 20th national conference on artificial intelligence, Pittsburgh, PA, USA."},{"key":"5035_CR18","doi-asserted-by":"crossref","first-page":"135","DOI":"10.7551\/mitpress\/1113.003.0012","volume-title":"Advances in large margin classifiers","author":"O. Mangasarian","year":"2000","unstructured":"Mangasarian, O. (2000). Generalized support vector machines. In A. Smola, P. Bartlett, B. Sch\u00f6lkopf, & D. Schuurmans (Eds.), Advances in large margin classifiers (pp. 135\u2013146). Cambridge: MIT Press."},{"issue":"1\u20133","key":"5035_CR19","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1023\/A:1012422931930","volume":"46","author":"O. L. Mangasarian","year":"2002","unstructured":"Mangasarian, O. L., & Musicant, D. R. (2002). Large scale kernel regression via linear programming. Machine Learning, 46(1\u20133), 255\u2013269.","journal-title":"Machine Learning"},{"key":"5035_CR20","first-page":"1127","volume":"5","author":"O. L. Mangasarian","year":"2004","unstructured":"Mangasarian, O. L., Shavlik, J. W., & Wild, E. W. (2004). Knowledge-based kernel approximation. Journal of Machine Learning Research, 5, 1127\u20131141.","journal-title":"Journal of Machine Learning Research"},{"key":"5035_CR21","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/TNN.2006.886354","volume":"18","author":"O. L. Mangasarian","year":"2007","unstructured":"Mangasarian, O. L., & Wild, E. W. (2007). Nonlinear knowledge in kernel approximation. IEEE Transactions on Neural Networks, 18, 300\u2013306.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"5035_CR22","first-page":"211","volume-title":"Advances in kernel methods: support vector learning","author":"D. Mattera","year":"1999","unstructured":"Mattera, D., & Haykin, S. (1999). Support vector machines for dynamic reconstruction of a chaotic system. In B. Sch\u00f6lkopf, C. J. Burges, & A. J. Smola (Eds.), Advances in kernel methods: support vector learning (pp. 211\u2013241). Cambridge: MIT Press."},{"key":"5035_CR23","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1137\/0909048","volume":"9","author":"C. Micchelli","year":"1988","unstructured":"Micchelli, C., & Utreras, F. (1988). Smoothing and interpolation in a convex subset of a Hilbert space. SIAM Journal on Scientific and Statistical Computing, 9, 728.","journal-title":"SIAM Journal on Scientific and Statistical Computing"},{"key":"5035_CR24","doi-asserted-by":"crossref","unstructured":"M\u00fcller, K., Smola, A., R\u00e4tsch, G., Sch\u00f6lkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines. In Proceedings of the international conference on artificial neural networks (pp.\u00a0999\u20131004).","DOI":"10.1007\/BFb0020283"},{"issue":"1","key":"5035_CR25","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/72.80202","volume":"1","author":"K. S. Narendra","year":"1990","unstructured":"Narendra, K. S., & Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1(1), 4\u201327.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"5035_CR26","unstructured":"Poggio, T., & Vetter, T. (1992). Recognition and structure from one 2D model view: observations on prototypes, object classes and symmetries (Technical Report AIM-1347). Massachusetts Institute of Technology, Cambridge, MA, USA."},{"issue":"8","key":"5035_CR27","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TSP.2004.831028","volume":"52","author":"M. S\u00e1nchez-Fern\u00e1ndez","year":"2004","unstructured":"S\u00e1nchez-Fern\u00e1ndez, M., De Prado-Cumplido, M., Arenas-Garc\u00eda, J., & P\u00e9rez-Cruz, F. (2004). SVM multiregression for nonlinear channel estimation in multiple-input multiple-output systems. IEEE Transactions on Signal Processing, 52(8), 2298\u20132307.","journal-title":"IEEE Transactions on Signal Processing"},{"key":"5035_CR28","series-title":"Lecture notes in computer science","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1007\/3-540-61510-5_12","volume-title":"ICANN","author":"B. Sch\u00f6lkopf","year":"1996","unstructured":"Sch\u00f6lkopf, B., Burges, C., & Vapnik, V. (1996). Incorporating invariances in support vector learning machines. In C. von der Malsburg, W. von Seelen J. C. Vorbr\u00fcggen, & B. Sendhoff (Eds.), Lecture notes in computer science : Vol.\u00a01112. ICANN (pp. 47\u201352). Berlin: Springer."},{"issue":"3","key":"5035_CR29","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/B:STCO.0000035301.49549.88","volume":"14","author":"A. J. Smola","year":"2004","unstructured":"Smola, A. J., & Sch\u00f6lkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199\u2013222.","journal-title":"Statistics and Computing"},{"issue":"4","key":"5035_CR30","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/S0893-6080(98)00032-X","volume":"11","author":"A. J. Smola","year":"1998","unstructured":"Smola, A. J., Sch\u00f6lkopf, B., & M\u00fcller, K. R. (1998). The connection between regularization operators and support vector kernels. Neural Networks, 11(4), 637\u2013649.","journal-title":"Neural Networks"},{"key":"5035_CR31","first-page":"585","volume-title":"Advances in neural information processing systems","author":"A. J. Smola","year":"1999","unstructured":"Smola, A. J., Friess, T., & Sch\u00f6lkopf, B. (1999a). Semiparametric support vector and linear programming machines. Advances in neural information processing systems (vol.\u00a011, pp. 585\u2013591). Cambridge: MIT Press."},{"key":"5035_CR32","doi-asserted-by":"crossref","unstructured":"Smola, A. J., Sch\u00f6lkopf, B., & R\u00e4tsch, G. (1999b). Linear programs for automatic accuracy control in regression. In Proceedings of the 9th international conference on artificial neural networks (vol.\u00a02, pp.\u00a0575\u2013580) Edinburgh, UK.","DOI":"10.1049\/cp:19991171"},{"key":"5035_CR33","volume-title":"System identification","author":"T. S\u00f6derstr\u00f6m","year":"1988","unstructured":"S\u00f6derstr\u00f6m, T., & Stoica, P. (1988). System identification. Upper Saddle River: Prentice-Hall."},{"key":"5035_CR34","first-page":"285","volume-title":"Advances in kernel methods: support vector learning","author":"M. O. Stitson","year":"1999","unstructured":"Stitson, M. O., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C., & Weston, J. (1999). Support vector regression with ANOVA decomposition kernels. In B. Sch\u00f6lkopf, C. J. Burges, & A. J. Smola (Eds.), Advances in kernel methods: support vector learning (pp. 285\u2013291). Cambridge: MIT Press."},{"key":"5035_CR35","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1016\/S0925-2312(01)00676-2","volume":"48","author":"F. Tay","year":"2002","unstructured":"Tay, F., & Cao, L. (2002). Modified support vector machines in financial time series forecasting. Neurocomputing, 48, 847\u2013861.","journal-title":"Neurocomputing"},{"issue":"1\u20132","key":"5035_CR36","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0004-3702(94)90105-8","volume":"70","author":"G. G. Towell","year":"1994","unstructured":"Towell, G. G., & Shavlik, J. W. (1994). Knowledge-based artificial neural networks. Artificial Intelligence, 70(1\u20132), 119\u2013165.","journal-title":"Artificial Intelligence"},{"key":"5035_CR37","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V. N. Vapnik","year":"1995","unstructured":"Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer."},{"issue":"397","key":"5035_CR38","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1080\/01621459.1987.10478426","volume":"82","author":"M. Villalobos","year":"1987","unstructured":"Villalobos, M., & Wahba, G. (1987). Inequality-constrained multivariate smoothing splines with application to the estimation of posterior probabilities. Journal of the American Statistical Association, 82(397), 239\u2013248.","journal-title":"Journal of the American Statistical Association"},{"key":"5035_CR39","unstructured":"Weston, J., Chapelle, O., Elisseeff, A., Scholkopf, B., & Vapnik, V. (2003). Kernel dependency estimation. Advances in neural information processing systems (Vol.\u00a015), pp.\u00a0873\u2013880."},{"key":"5035_CR40","first-page":"293","volume-title":"Advances in kernel methods: support vector learning","author":"J. Weston","year":"1999","unstructured":"Weston, J., Gammerman, A., Stitson, M. O., Vapnik, V., Vovk, V., & Watkins, C. (1999). Support vector density estimation. In Sch\u00f6lkopf, B., Burges, C. J. & Smola, A. J. (Eds.), Advances in kernel methods: support vector learning (pp. 293\u2013305). Cambridge: MIT Press."},{"key":"5035_CR41","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1145\/1014052.1014089","volume-title":"Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining","author":"X. Wu","year":"2004","unstructured":"Wu, X., & Srihari, R. (2004). Incorporating prior knowledge with weighted margin support vector machines. In Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 326\u2013333), Seatle WA, USA. New York: ACM Press."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-007-5035-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-007-5035-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-007-5035-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T04:56:39Z","timestamp":1708318599000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-007-5035-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2007,11,17]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2007,12,6]]}},"alternative-id":["5035"],"URL":"https:\/\/doi.org\/10.1007\/s10994-007-5035-5","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2007,11,17]]}}}