{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T22:22:15Z","timestamp":1769811735638,"version":"3.49.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1-3","license":[{"start":{"date-parts":[[2002,7,1]],"date-time":"2002-07-01T00:00:00Z","timestamp":1025481600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2002,7,1]],"date-time":"2002-07-01T00:00:00Z","timestamp":1025481600000},"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":["Machine Learning"],"published-print":{"date-parts":[[2002,7]]},"DOI":"10.1023\/a:1013903804720","type":"journal-article","created":{"date-parts":[[2002,12,28]],"date-time":"2002-12-28T13:55:48Z","timestamp":1041083748000},"page":"137-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Structural Modelling with Sparse Kernels"],"prefix":"10.1007","volume":"48","author":[{"given":"S.R.","family":"Gunn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.S.","family":"Kandola","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"393394_CR1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1090\/S0002-9947-1950-0051437-7","volume":"686","author":"N. Aronszajn","year":"1950","unstructured":"Aronszajn, N. (1950). Theory of reproducing kernels. Trans. Amer. Math. Soc., 686, 337\u2013404.","journal-title":"Trans. Amer. Math. Soc."},{"key":"393394_CR2","doi-asserted-by":"crossref","DOI":"10.1515\/9781400874668","volume-title":"Adaptive control processes","author":"R. Bellman","year":"1961","unstructured":"Bellman, R. (1961). Adaptive control processes. Princeton, NJ: Princeton University Press."},{"key":"393394_CR3","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural networks for pattern recognition","author":"C. Bishop","year":"1995","unstructured":"Bishop, C. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press."},{"key":"393394_CR4","unstructured":"Blake, C., & Merz, C. (1998). UCI Repository of machine learning databases."},{"key":"393394_CR5","unstructured":"Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Wadsworth Inc."},{"key":"393394_CR6","volume-title":"Neurofuzzy adaptive modelling and control","author":"M. Brown","year":"1994","unstructured":"Brown, M., & Harris, C. J. (1994). Neurofuzzy adaptive modelling and control. Hemel Hempstead: Prentice Hall."},{"key":"393394_CR7","first-page":"52","volume-title":"Proc. Seventh Annual Conference on Uncertainty Artificial Intelligence. San Francisco, CA","author":"W. Buntine","year":"1991","unstructured":"Buntine, W. (1991). Theory refinement on bayesian networks. In B. D. D'Ambrosio, P. Smets, & P. P. Bonissone (Eds.), Proc. Seventh Annual Conference on Uncertainty Artificial Intelligence. San Francisco, CA, (pp. 52\u201360), San Mateo, CA: Morgan Kaufmann Publishers."},{"key":"393394_CR8","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","volume":"2","author":"C. Burges","year":"1998","unstructured":"Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Journal of Data Mining and Knowledge Discovery, 2, 121\u2013167.","journal-title":"Journal of Data Mining and Knowledge Discovery"},{"key":"393394_CR9","unstructured":"Chen, S. (1995). Basis pursuit. Ph.D. thesis, Department of Statistics, Stanford University."},{"key":"393394_CR10","volume-title":"An introduction to support vector machines","author":"N. Cristianini","year":"2000","unstructured":"Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines. Cambridge: Cambridge University Press."},{"key":"393394_CR11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/0004-3702(93)90036-B","volume":"60","author":"P. Dagum","year":"1993","unstructured":"Dagum, P., & Luby, M. (1993). Approximating probabilistic inference in bayesian belief networks is NP-hard. Artificial Intelligence, 60, 141\u2013153.","journal-title":"Artificial Intelligence"},{"issue":"1","key":"393394_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1979.tb01052.x","volume":"41","author":"A. Dawid","year":"1979","unstructured":"Dawid, A. (1979a). Conditional independence in statistical theory (with discussion). Journal of the Royal Statistical Society B, 41:1, 1\u201331.","journal-title":"Journal of the Royal Statistical Society B"},{"issue":"2","key":"393394_CR13","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1111\/j.2517-6161.1979.tb01079.x","volume":"41","author":"A. Dawid","year":"1979","unstructured":"Dawid, A. (1979b). Some misleading arguments concerning conditional independence. Journal of the Royal Statistical Society B, 41:2, 249\u2013252.","journal-title":"Journal of the Royal Statistical Society B"},{"key":"393394_CR14","first-page":"1","volume":"19","author":"J. Friedman","year":"1991","unstructured":"Friedman, J. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19, 1\u2013141.","journal-title":"The Annals of Statistics"},{"key":"393394_CR15","unstructured":"Friedman, N., & Nachman, N. (2000). Gaussian process networks. In Proc. Sixteenth Conf. on Uncertainty in Artificial Intelligence (UAI), to appear."},{"key":"393394_CR16","unstructured":"Girosi, F. (1997). An equivalence between sparse approximation and support vector machines. A.I. Memo 1606, MIT Artificial Intelligence Laboratory."},{"key":"393394_CR17","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1162\/neco.1995.7.2.219","volume":"7","author":"F. Girosi","year":"1995","unstructured":"Girosi, F., Jones, M., & Poggio, T. (1995). Regularization theory and neural networks architectures. Neural Computation, 7, 219\u2013269.","journal-title":"Neural Computation"},{"key":"393394_CR18","volume-title":"Maximum entropy and bayesian methods","author":"S. Gull","year":"1989","unstructured":"Gull, S. (1989). Developments in maximum entropy data analysis. In J. Skilling (Ed.), Maximum entropy and bayesian methods. Dordrecht: Kluwer Academic Publishers."},{"key":"393394_CR19","unstructured":"Gunn, S. R. (1998). Support vector machines for classification and regression. Technical Report ISIS-1-98, Department of Electronics and Computer Science, University of Southampton."},{"key":"393394_CR20","doi-asserted-by":"crossref","unstructured":"Gunn, S. R., Brown, M., & Bossley, K. M. (1997). Network performance assessment for neurofuzzy data modelling. In Intelligent Data Analysis, (pp. 313-323).","DOI":"10.1007\/BFb0052850"},{"key":"393394_CR21","unstructured":"Hadamard, J. (1923). Lectures on the cauchy problem in linear partial differential equations. Yale University Press."},{"key":"393394_CR22","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/0095-0696(78)90006-2","volume":"5","author":"D. Harrison","year":"1978","unstructured":"Harrison, D., & Rubinfield, D. (1978). Hedonic housing prices and the demand for clean air. Journal of Enviromental Economics and Management, (5), 81\u2013102.","journal-title":"Journal of Enviromental Economics and Management"},{"key":"393394_CR23","volume-title":"Learning in graphical models","author":"D. Heckerman","year":"1999","unstructured":"Heckerman, D. (1999). A tutorial on learning with bayesian network, Learning in graphical models, Cambridge, MA: MIT Press."},{"key":"393394_CR24","first-page":"197","volume":"20","author":"D. Heckerman","year":"1995","unstructured":"Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 197\u2013243.","journal-title":"Machine Learning"},{"key":"393394_CR25","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4471-0847-4","volume-title":"Neural networks for conditional probability estimation","author":"D. Husmeier","year":"1999","unstructured":"Husmeier, D. (1999). Neural networks for conditional probability estimation. Berlin: Springer-Verlag Publishers."},{"key":"393394_CR26","unstructured":"Kandola, J. S., & Gunn, S. R. (2000). On the use of advanced inductive methods for knowledge extraction from complex datasets. Submitted to Journal of Data Mining and Knowledge Discovery."},{"key":"393394_CR27","series-title":"Springer series on Advances in Industrial Control","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/978-1-4471-3066-6_5","volume-title":"Advances in neural networks for control systems","author":"T. Kavli","year":"1995","unstructured":"Kavli, T., & Weyer, E. (1995). On ASMOD-an algorithm for building multivariable spline models. In G. I. K. J. Hunt & K. Warwick (Eds.), Advances in neural networks for control systems, Springer series on Advances in Industrial Control. Berlin: Springer Verlag, pp. 83\u2013104."},{"key":"393394_CR28","volume-title":"Graphical models","author":"S. Lauritzen","year":"1995","unstructured":"Lauritzen, S. (1995). Graphical models. Oxford: Oxford University Press."},{"key":"393394_CR29","unstructured":"MacKay, D. (1994). Bayesian non-linear modelling for the prediction competition. ASHRAE Transactions: Symposia, OR-94-17-1."},{"key":"393394_CR30","unstructured":"MacKay, D. (1995). Ensemble learning and evidence maximization. Technical Report, Cavendish Laboratory, Dept. Physics, University of Cambridge."},{"key":"393394_CR31","unstructured":"M\u00e9xsz\u00e1ros, C. (1998). The BPMPD interior point solver for convex quadratic problems. Technical Report WP 98-8, Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest."},{"key":"393394_CR32","unstructured":"Moody, J. E., & Rognvaldsson, T. S. (1996). Smoothing regularisers for projective basis function networks. Technical Report OGI CSE TR 96-006, Dept. Computer Science and Engineering, Oregan Graduate Institute of Science and Technology."},{"key":"393394_CR33","volume-title":"Bayesian learning for neural networks","author":"R. Neal","year":"1995","unstructured":"Neal, R. (1995). Bayesian learning for neural networks. Berlin: Springer-Verlag Publishers."},{"key":"393394_CR34","volume-title":"Probabilistic reasoning in intelligent systems","author":"J. Pearl","year":"1988","unstructured":"Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo, CA: Morgan Kaufmann Publishers."},{"key":"393394_CR35","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/S0893-6080(99)00040-4","volume":"12","author":"W. Penny","year":"1998","unstructured":"Penny, W., Roberts, S. (1998). Bayesian classification using neural networks-how useful is the evidence framework. Neural Networks, 12, 877\u2013892.","journal-title":"Neural Networks"},{"key":"393394_CR36","first-page":"29","volume":"26","author":"T. Plate","year":"1999","unstructured":"Plate, T. (1999). Accuracy versus interpretability in flexible modelling: Implementing a tradeoff using Gaussian process models. Behaviourmetrika special issue on \u201cInterpreting Neural Network Models,\u201d 26, 29\u201350.","journal-title":"Behaviourmetrika special issue on \u201cInterpreting Neural Network Models,\u201d"},{"key":"393394_CR37","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/317314a0","volume":"317","author":"T. Poggio","year":"1985","unstructured":"Poggio, T., Torre, V., & Koch, C. (1985). Computational vision and regularization theory. Nature, 317, 314\u2013319.","journal-title":"Nature"},{"key":"393394_CR38","first-page":"81","volume":"1","author":"J. Quinlan","year":"1986","unstructured":"Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1, 81\u2013106.","journal-title":"Machine Learning"},{"key":"393394_CR39","unstructured":"Rasmussen, C. (1996). Evaluation of gaussian processes and other methods for nonlinear regression."},{"key":"393394_CR40","first-page":"79","volume-title":"Proc. of the Ninth Australian Conf. on Neural Networks","author":"A. Smola","year":"1998","unstructured":"Smola, A., Sch\u00f6lkopf, B., & M\u00fcller, K.-R. (1998). General cost functions for support vector regression. In T. Downs, M. Frean, & M. Gallagher (Eds.), Proc. of the Ninth Australian Conf. on Neural Networks. Brisbane, Australia (pp. 79\u201383). University of Queensland."},{"key":"393394_CR41","unstructured":"Smola, A. J. (1998). Learning with Kernels. Ph.D. thesis, Technische Universit\u00fct Berlin."},{"key":"393394_CR42","volume-title":"Advances in Kernel methods-support vector learning","author":"M. Stitson","year":"1999","unstructured":"Stitson, M., Gammerman, A., Vapnik, V., Vovk, V., Watkins, C., & Weston, J. (1999). Support vector regression with ANOVA decomposition kernels. In B. Sch\u00f6lkopf, C. J. C. Burges, & A. J. Smola (Eds.), Advances in Kernel methods-support vector learning. Cambridge, MA (pp. 285-292). Cambridge, MA: MIT Press."},{"key":"393394_CR43","volume-title":"Solutions of ill-posed problems","author":"A. N. Tikhonov","year":"1977","unstructured":"Tikhonov, A. N., & Arsenin, V. Y. (1977). Solutions of ill-posed problems. Washington, D.C.: W. H. Winston."},{"key":"393394_CR44","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"V. Vapnik","year":"1995","unstructured":"Vapnik, V. (1995). The nature of statistical learning theory. New York: Springer."},{"key":"393394_CR45","doi-asserted-by":"crossref","unstructured":"Wahba, G. (1990). Splines models for observational data. Philadelphia: Series in Applied Mathematics, Vol. 59, SIAM.","DOI":"10.1137\/1.9781611970128"},{"key":"393394_CR46","volume-title":"Advances in neural information processing (NIPS), vol. 6","author":"G. Wahba","year":"1994","unstructured":"Wahba, G., Wang, Y., Gu, C., Klein, R., & Klein, B. (1994). Structured machine learning for 'soft' classification with smoothing spline ANOVA and stacked tuning, testing and evaluation. In J. Cowan, G. Tesaro & J. Alspector (Eds.), Advances in neural information processing (NIPS), vol. 6, San Mateo, CA: Morgan Kauffman."},{"key":"393394_CR47","volume-title":"Graphical models in applied multivariate statistics","author":"J. Whittaker","year":"1990","unstructured":"Whittaker, J. (1990). Graphical models in applied multivariate statistics. Chichester, UK: John Wiley and Sons."},{"key":"393394_CR48","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1016\/S0140-6736(95)92893-6","volume":"346","author":"J. Wyatt","year":"1995","unstructured":"Wyatt, J. (1995). Nervous about artificial neural networks? (commentary). The Lancet, 346, 1175\u20131177.","journal-title":"The Lancet"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1023\/A:1013903804720.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1023\/A:1013903804720\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1023\/A:1013903804720.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T11:32:11Z","timestamp":1752147131000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1023\/A:1013903804720"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,7]]},"references-count":48,"journal-issue":{"issue":"1-3","published-print":{"date-parts":[[2002,7]]}},"alternative-id":["393394"],"URL":"https:\/\/doi.org\/10.1023\/a:1013903804720","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,7]]},"assertion":[{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}